Ralf Karam, Lara Ammoury
Department of Political, Economic and Social Sciences, University of Milan, Milan, Italy.
DOI: 10.4236/aasoci.2025.158042   PDF    HTML   XML   331 Downloads   2,141 Views   Citations

Abstract

The objective of this study is to explore the role of Artificial Intelligence (AI) in enhancing tax compliance and planning. Using a quantitative survey with 109 responses from professionals and students, the study examines the perceived benefits, challenges, and future outlook of AI in tax processes. Key findings indicate AI’s significant advantages, such as time savings, improved accuracy, and tax-saving opportunities. However, challenges like high costs, data security concerns, and tool complexity persist. A one-sample t-test showed AI’s impact on ten out of eleven identified variables, with time reduction (M = 4.22) being the highest-ranked effect. While simplicity and compliance with regulations also scored high, difficulty of use showed neutral significance. A Pearson correlation analysis revealed no link between age and future AI implementation. The study suggests policymakers adopt AI in tax systems but address security concerns, while future research should improve AI algorithms and integration with financial systems.

Keywords

Tax, AI, Artificial Intelligence, Tax Compliance, Tax Planning

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1. Introduction

Artificial intelligence (AI) is the trend nowadays, and the compliance and planning processes sector is not an exception to this trend; AI is revolutionizing the tax sector. According to the OECD, using AI in tax administrations can substantially enhance efficiency, accuracy, and compliance (Tax Administration 3.0: The Digital Transformation of Tax Administration, 2020). For instance, adopting AI by the Internal Revenue Service (IRS) has generated better tax compliance rates. Similarly, in European Union (EU) countries, AI systems have played a critical role in detecting and combating tax evasion, improving overall compliance with tax obligations (Artificial Intelligence (AI) Act: Council Gives Final Green Light to the First Worldwide Rules on AI, 2024).

Given the great interest in the subject by researchers from different backgrounds, applying AI to the field of tax compliance and planning, academics and practitioners are increasingly exploring several approaches that strive to seek better strategies for improved accuracy, efficiency, and compliance (Abhishek et al., 2024; Nembe et al., 2024). For example, machine learning algorithms can be utilized for extensive information search, which will then be used to predict tax liabilities and optimize the tax strategy; Natural Language Processing (NLP) tools can be used for interpretation of complex tax regulations and assurance of the adherence to legal requirements (Rane et al., 2024). AI systems could offer real-time insights and evidence-based recommendations to guide businesses on informed decisions on tax planning and compliance (Nembe et al., 2024; Secinaro et al., 2024).

The primary objective of the research is to explore and analyze the utilization of AI technologies in streamlining tax compliance processes, identifying tax-saving opportunities, optimizing tax strategies, and ensuring compliance with changing tax regulations, specifically:

1) To map the key effects of AI in tax planning and compliance.

2) To investigate the role of AI-driven tax solutions in identifying tax-saving opportunities and strategies.

3) To evaluate the effectiveness of AI in ensuring compliance with changing tax regulations.

4) To assess which age group is more receptive to the future implementation of AI-driven tax solutions.

5) To rank based on the level of importance the results of usage of AI-driven tax solutions in order of importance.

Therefore, the research questions are the following:

RQ1: What are the key effects of AI usage in tax planning and compliance?

RQ2: What role does AI-powered tax software play in identifying tax-saving opportunities and optimizing tax strategies?

RQ3: How effective is AI in ensuring compliance with changing tax regulations?

RQ4: How does age affect the receptiveness of future implementation of AI driven tax solutions?

RQ5: What is the rank and level of importance of the results of AI usage in tax-driven solutions in order of importance?

This proposed research stands out in quite a few ways and contributes to new insights and perspectives into the existing body of knowledge. Even though the literature on AI applications in various fields shows dynamic development, it has not provided a comprehensive examination of how exactly AI technologies impact the tax compliance process including the level of importance of these effects. This research aims to fill this gap and to offer detailed insights into concerning the role of AI-powered tax software in identifying tax-saving opportunities and optimizing tax strategies. More importantly, the dynamic element of this research explores further ways in which AI ensures compliance with changing tax regulations. Additionally, the quantitative research approach in data collection has provided empirical evidence to help establish the hypotheses; this approach increases the credibility and validity of the findings and forms a sound basis for analyzing the role played by AI in tax compliance.

Despite the developments in AI and its various applications, there are several literature gaps concerning the deployment of AI in tax compliance and planning, for instance empirical studies that assess how much AI affects tax compliance processes are rare, and most of the available research is theoretical based or case study oriented with limited generalizability. Furthermore, the literature on AI in tax compliance is fragmented, with studies often focusing on specific aspects such as fraud detection or data analysis, missing a holistic view that integrates these elements into a coherent understanding of the role of AI across the tax compliance spectrum. In addition, the dynamics of tax regulations require continuous adaptation. However, there is limited research on how AI can keep pace with and ensure compliance with these evolving laws. This research addresses how AI can dynamically respond to changes in tax regulations, an area that is not yet well-researched. Lastly, there is a gap in understanding the perceptions, attitudes and adoption rates of AI technologies among tax professionals and businesses. Examining these factors can provide insights into the barriers and facilitators of AI adoption in tax compliance.

This paper is organized as follows: Section 2 is the literature review, Section 3 describes the methodology, Section 4 provides data interpretation, comments and criticisms on the main findings, and Section 5 concludes the article with a summary and recommendations for future research.

2. Literature Review

The use of AI in tax compliance is receiving a lot of attention. Scholars have explored the potential of AI to enhance the accuracy and efficiency of tax processes, the focus was mainly on the applicability of AI in improving tax information accuracy (Acemoglu, 2024). Research has shown that younger taxpayers are more receptive to AI-driven tax solutions, which leads to an assumption regarding demographic trend in technology adoption.

AI technologies have been employed to automate various aspects of tax compliance, such as data collection, processing, and analysis (Nembe et al., 2024). Risse (2023) found that AI applications in tax compliance face technological and stakeholder acceptance challenges. Ajzen’s (1991) Theory of Planned Behavior (TPB) explains the factors affecting the adoption of AI in tax compliance, emphasizing the importance of user acceptance. Technical solutions alone are insufficient without addressing user acceptance. Despite the rational benefits of AI technologies, trust issues remain a major barrier (Parihar, 2024).

AI can significantly reduce the time needed for tax preparation, filing, and reporting (Kluwer, 2024). AI systems can handle time-consuming tasks more efficiently than humans, resulting in a more effective outcome of fewer errors and faster outcomes. Furthermore, advanced AI algorithms can constantly learn and improve, enhancing their efficiency and accuracy even further (Manyika, 2018).

AI-powered tax software plays a crucial role in identifying tax-saving opportunities by analyzing large amounts of data and revealing patterns that may not be obvious to human tax professionals (Huang et al., 2024; Secinaro et al., 2023; Wilkin & Chenhall, 2010). Furthermore, businesses adopting AI in tax planning benefit from enhanced decision-making capabilities, driven by the software’s capabilities to process complex data and provide actionable awareness (Abdullah & Almaqtari, 2024). These systems help organizations optimize their tax strategies by leveraging machine learning algorithms. By automating the analysis of tax laws and regulations, AI-powered tax software can identify opportunities for tax savings that humans may miss. Additionally, these systems can simulate various tax scenarios, allowing companies to adopt the most advantageous tax strategies.

The effectiveness of AI in ensuring compliance with changing tax regulations is very important to explore (Adelekan et al., 2024). AI systems can be programmed to update new tax laws and rules automatically to help organizations comply with the most recent tax filings (Belahouaoui & Attak, 2024). This dynamic adaptability is especially useful for companies operating in environments with frequently changing tax codes (Bentley, 2020). Despite lacking trust in AI tax compliance, AI’s potential to maintain up-to-date compliance with changing regulations is a key advantage. Clear public policies and regulatory frameworks are essential to support the integration of AI in tax compliance (Abdullah & Almaqtari, 2024). Furthermore, AI can enhance compliance by providing real-time alerts and recommendations, reducing the risk of non-compliance and associated penalties (Wang et al., 2020).

In summary, the literature on AI in tax compliance and planning reveals two primary streams: the enhancement of tax compliance through AI technologies and the barriers to their adoption. AI technologies significantly streamline tax compliance processes, identify tax-saving opportunities, and ensure compliance with changing regulations. However, challenges such as stakeholder trust and socio-technical barriers must be addressed to fully realize AI’s potential in tax compliance (Wang et al., 2020).

Considering the multitude of perspectives in which the topic is covered by academics and the fragmentation of the existing knowledge, a literature review appears relevant among identified elements by systematizing the flow of knowledge. It is essential to map the key factors influencing attitudes towards AI in tax planning and compliance. This study provides advanced study by collecting recent information that previous authors have not provided. Below are the hypotheses generated from the literature review and the conceptual diagram sketched accordingly to be tested in Figure 1.

H1: AI technologies reduce the time required for tax compliance.

H2: AI increase the accuracy in filing tax returns.

H3: AI tax software simplifies the tax preparation process.

H4: AI technologies create concerns regarding security and privacy issues in tax compliance and planning processes than traditional methods.

H5: AI technologies can replace the expertise of human tax professionals.

H6: There is a lack of trust in AI’s recommendations in tax compliance and planning.

H7: AI technologies are too expensive to be used in tax compliance and planning processes than traditional methods.

H8: AI-powered tax software enhances the identification of tax-saving opportunities, leading to optimized tax strategies.

H9: AI-powered tax software provides better compliance with changing tax regulations.

H10: AI-powered tax software will be widely used in the future.

H11: AI-powered tax software is difficult to use.

H12: Younger generations are more receptive to the future implementation of AI-driven tax solutions, suggesting a demographic trend in AI technology adoption.

Figure 1. Conceptual diagram, source: author.

3. Methodology

A quantitative survey questionnaire methodology was employed to explore these research questions and test the hypotheses (Ghanad, 2023). The survey targeted professionals and business and finance major’ university students and included sections to assess familiarity with AI, usage of AI-powered tax software, perceived benefits, challenges, and future perspectives.

3.1. Research Design

The research utilized a quantitative survey methodology to gather data on using AI technologies in tax compliance and planning, this approach allows for the collection of structured and measurable data, facilitating the analysis of trends, perceptions, and experiences related to AI-powered tax software (Lim, 2024).

3.2. Survey Instrument

A structured questionnaire was developed using Google Forms, comprising multiple-choice questions, 5-Likert scale statements, and Yes-No questions (Darnton, 2023). The survey is divided into six sections: a cover letter, demographic information, awareness and usage of AI in tax compliance, perceived benefits of AI in tax compliance and planning, challenges and concerns, and future perspectives.

3.3. Sampling

This study employed a purposeful sampling technique. The survey targeted a diverse audience, including students, employed individuals in tax-related and non-tax-related fields, self-employed, and retirees. This broad sampling strategy is designed to gather perspectives from a wide range of respondents, ensuring the generalizability of the findings and the increase in sample size.

3.4. Data Collection

The survey was distributed electronically via email, WhatsApp, and LinkedIn to reach 980 broad audience. Participants were invited to complete the survey voluntarily. Electronic distribution ensures efficient data collection and the ability to reach respondents across different geographic locations using a cross-sectional descriptive study design. Quantitative survey responses were collected at one time point only, and 109 valid respondents were captured.

3.5. Data Coding and Analysis

Quantitative data collected from the survey was analyzed using Excel and google forms statistical software. The Likert scale responses to measure the level of agreement or disagreement were summarized, and the survey data were coded into numerical values, ranging from 1 to 5 for the Likert scale statements and 1 to 6 for the age interval variable. Descriptive statistics, such as means, frequencies, and percentages, were used to summarize the data and check the normality and reliability of the data, and inferential statistics tests were utilized to test the twelve hypotheses and to identify common themes and insights. Relative importance index (RII) technique was utilized to rank the results based on the perception of AI usage in tax-driven solutions from the highest to the lowest influence to answer the research question RQ5 (Ammoury, 2023). A correlation test was utilized to verify the correlation between age and future implementation of AI-driven tax solutions and test H12₀ (Lu & Yuan, 2024). A two-tailed one-sample t-test was conducted to identify the existence of a relation between the eleven identified independent variables and the AI in tax compliance and planning (Kronthaler, 2024), to test H1₀, H2₀, H3₀, H4₀, H5₀, H6₀, H7₀, H8₀, H9₀, H10₀ and H11₀. The nineteen variables in the dataset are the were used for descriptive analysis and to test the hypotheses and the conceptual diagram: Independent Variable (IV), Dependent Variable (DV).

• Twelve impact variables (DV): Time reduction, Simplicity, Security and privacy issues, Tax-saving opportunities, Receptiveness of future implementation of AI-driven tax solutions, Replacing human expertise, Compliance with tax regulations, Accuracy, Familiarity with AI, Lack of trust, High cost, Difficulty to use (5-point Likert scale).

• Two variables: Familiarity with AI in tax and Usage of AI in tax (Yes/No questions).

• One variable (DV): Future implementation of AI in tax compliance and planning (4-level answers).

• Four demographic variables coded numerically from one to six (IV): Age (interval variable), level of education, Occupation and Work experience (coded one to five).

3.6. Ethical Considerations

Participation in the survey was voluntary, and respondents were assured of the confidentiality and anonymity of their responses. Informed consent was obtained by reading the cover letter and proceeding to the next section of the survey. Participants were also informed about the purpose of the research, the use of the data collected, and their right to withdraw from the study at any time.

3.7. Limitations

The study acknowledges potential limitations, such as self-selection bias, as participants more interested in AI or tax compliance might be more likely to respond. Additionally, the sample size is small of 109 respondents. Lastly, the rationale for including business and finance major’ students is justified because tax professionals would have a background of business and finance major, but this mixed sample in a study on professional tax practice might affect the results’ generalizability and hence another limitation of the study.

4. Analysis

This section breaks down the survey results in percentage. The survey collected data about demographics, familiarity with AI, awareness and usage of AI in tax compliance, perceived benefits, challenges, and future perspectives.

4.1. Descriptive Analysis

The survey received responses from a diverse group of demographic participants as follows. Figure 2 shows that the age is diverse with the majority of respondents being under 25 (38.5 percent), between 25 - 34 years old (10.1 percent), 35 - 44 years old (13.8 percent), a higher proportion of respondents are between 45 - 54 years old (26.6 percent), which indicates that the respondents are relatively young. Figure 3 shows that most participants have a bachelor’s degree (44 percent) and additionally a master’s degree (32.1 percent), a significant number of respondents held a Doctorate (6.5 percent), while a few had only completed high school or currently enrolled in university (11 percent), which indicates that most of the respondents have a good education level. Figure 4 shows that majority of the participants included students (33.9 percent), self-employed individuals (10.1 percent), retired (7.3 percent), and employed individuals both in tax-related (15.6 percent) and non-tax-related fields (32.1 percent), which indicates that most respondents are working professionals and 15.6 percent are specifically working in tax related fields. Figure 5 shows that respondents have varied levels of experience in their fields, with the largest group having more than 10 years of experience (51.4 percent), followed by those with less than 1 year (24.8 percent), 1 - 3 years (14.7 percent), which indicates that the majority have good work experience. Figure 6 shows that the familiarity with AI technologies among respondents varied, only 13.8 percent of respondents reported being very familiar with AI, about 33.9 percent were somewhat familiar, while 32 percent had moderate familiarity, 14.7 percent were slightly familiar, and 8.3 percent were not familiar at all, which indicates that most respondents have moderate to somewhat familiarity about AI in general. Figure 7 shows that the majority (67 percent) are not aware of AI technologies in tax compliance, 33 percent of respondents were aware of AI technologies being used in tax compliance and planning. Figure 8 shows that the usage of AI-powered tax software among respondents was limited to only 2.8 percent, indicating a significant opportunity for growth in this area. This means that among the seventeen tax related professionals (15.6 percent, Figure 4), three of them use AI power tax software. Figure 9 indicates that most respondents agree to the perceived benefits of AI in tax, that AI tax software simplifies the tax preparation process, helps in accurate filing tax returns, reduces the time spent on tax compliance, identifies tax savings opportunities and provides better compliance with changing tax regulations. Figure 10 shows classifies the most perceived benefits of AI in tax compliance among respondents: time savings in tax preparation, filing, and reporting (79.8 percent) was the highest chosen benefit, better improved data analysis and reporting (48.6 percent), improved accuracy in tax calculations and compliance (40.4 percent), cost reduction (39.4 percent), enhanced ability to identify tax-saving opportunities (27.5 percent), enhanced compliance with regulations (26.6 percent). Figure 11 shows the participants’ agreement to the highlighted challenges and concerns related to AI in tax compliance: data privacy and security concerns were the most agreed-to, followed by the perception that AI cannot fully replace the expertise of human tax professionals and then by lack of trust in AI, which indicates the resistance to change to AI in tax. Figure 12 shows the classification of the most important features that should be present in future AI-tax software, the majority of respondents (60.6 percent) think that AI-powered tax software should have regular updates on tax regulations, (57.8 percent) think it should have more robust data security, (55 percent) think that it should have better integration with financial software, (50.5 percent) think it should have more personalized tax savings recommendations, (47.7 percent) think it should have improved user interface and (42.2 percent) think it should have enhanced customer experience. Figure 13 shows that most respondents (37.6 percent) think that it is very likely that AI-powered tax software will be used in the future, and (36.7 percent) think it is likely, which means that the majority (74.3 percent) think that AI-powered tax software will be used in the future. Figure 14 shows that despite the resistance to change to AI, most respondents (48.6 percent) believe that AI would be a useful tool but not essential, followed by (40.4 percent) who thought that AI will become essential for all tax compliance and planning tasks, 11 percent were skeptical about the widespread adoption of AI in this field.

Figure 2. Age of respondents.

Figure 3. Level of education of respondents.

Figure 4. Occupation of respondents.

Figure 5. Level of experience of the respondents.

Figure 6. Familiarity with AI in general.

Figure 7. Awareness of AI usage in tax compliance and planning.

Figure 8. Usage of AI tax software.

Figure 9. Perceived benefits of AI in tax compliance.

Figure 10. Classification of perceived benefits of AI in tax compliance.

Figure 11. Challenges and concerns related to AI in tax compliance.

Figure 12. Future features in AI-powered tax software.

Figure 13. Receptiveness of the future usage of AI in tax.

Figure 14. Usefulness of AI in the future of tax compliance and planning.

4.2. Reliability and Validity Tests

The survey was shared with two professors for validity prior to distribution. For the reliability of the datasets, Cronbach’s alpha was calculated and found to be 0.6, therefore the data is questionable (Tavakol & Dennick, 2011).

Table 1 shows the calculated means, median, variance, standard deviation, range, minimum, maximum and sum for the fourteen variables (related to fourteen statements), the results are sorted in the order of agreement of the respondents from highest agreement to lowest agreement with the fourteen statements (corresponding variables). Notably, the results show that most respondents found that Time reduction (M = 4.22, SD = 0.77) is the highest effect of AI usage in tax-driven solutions, followed by Simplicity (M = 3.92, SD = 0.76), Compliance with changing tax regulations (M = 3.92, SD = 0.82) and Security and privacy issues (M = 3.92, SD = 0.95), all three with equal mean in second place and the least effect of AI using in tax-driven solutions is Difficulty to use (M = 2.88, SD = 0.89). Familiarity with AI in tax and Usage of AI in tax were yes/no statements with 2 responses; therefore, they will not be considered in future analysis.

Table 1. Descriptive statistics.

Statements	Mean	Std. Err.	Median	Mode	Std. Dev.	Var.	Kurtosis	Skewness	Min	Max	Sum
Time reduction	4.22	0.07	4.00	4.00	0.77	0.60	1.79	−1.01	1.00	5.00	460
Simplicity	3.92	0.07	4.00	4.00	0.76	0.58	0.65	−0.38	1.00	5.00	427
Compliance with changing tax regulations	3.92	0.08	4.00	4.00	0.82	0.67	0.57	−0.57	1.00	5.00	427
Security and privacy issues	3.92	0.09	4.00	4.00	0.95	0.91	0.16	−0.81	1.00	5.00	427
Tax-saving opportunities	3.90	0.08	4.00	4.00	0.85	0.72	0.30	−0.54	1.00	5.00	425
Receptiveness of future implementation of AI-driven tax solutions	3.84	0.12	4.00	5.00	1.27	1.61	−0.06	−1.03	1.00	5.00	419
Replacing human expertise	3.75	0.10	4.00	4.00	1.03	1.06	−0.09	−0.63	1.00	5.00	409
Accuracy	3.69	0.08	4.00	4.00	0.87	0.75	−0.17	−0.21	1.00	5.00	402
Lack of trust	3.39	0.09	3.00	4.00	0.94	0.89	−0.21	−0.37	1.00	5.00	369
High cost	3.36	0.08	3.00	3.00	0.83	0.69	−0.37	0.32	2.00	5.00	366
Familiarity with AI	3.26	0.11	3.00	3.00	1.13	1.27	−0.51	−0.29	1.00	5.00	355
Difficulty to use	2.88	0.09	3.00	3.00	0.89	0.79	0.69	0.16	1.00	5.00	314
Familiarity with AI in tax	1.33	0.05	1.00	1.00	0.47	0.22	−1.49	0.73	1.00	2.00	145
Usage of AI in tax	1.03	0.02	1.00	1.00	0.16	0.03	32.91	5.86	1.00	2.00	112

Table 1 also shows that the means values of the six variables are very close to the median values, in addition the Kurtosis is between −7 and +7 for all variables except for the variable “usage of AI in tax”, and the skewness is between −2 and +2 for all variables, except for the variable “usage of AI in tax”, this means that the data follows a normal distribution or all thirteen other variables, so we can proceed with parametric tests for these variables.

4.3. Inferential Tests

4.3.1. Relative Importance Index (RII)

To answer to the research question RQ5 “What is the rank and level of importance of the results of AI usage in tax-driven solutions in order of importance”, the researcher conducted an analysis to rank the results based on the perception of AI usage in tax-driven solutions from the highest to the lowest influence. Therefore, the most important factor was ranked “1” and the least important factor was ranked “10”. RII was calculated to determine the weighted value of each of the eleven variables that affect the use of AI in tax-driven solutions. It was computed by applying the formula described by Aziz et al. (2016):

$\left(\text{RII}\right)=\sum \text{W}/\left(\text{A}\times \text{T}\right)$

W = the weight given by every respondent to the specific variable, ranging from 1 to 5.

A = the maximum possible weight, which is 5.

T = the total number of acquired valid questionnaire responses, which is 109.

The value of RII ranges between zero and one. The higher the RII value, the more the variable is important in influencing results of AI usage in tax-driven solutions in order of importance.

Table 2. RII, ranking of the 10 variable in order of importance and their level of importance.

Variable	RII	Ranking	Level of Importance
Time reduction	0.84	1	Very important
Simplicity	0.78	2	Very important
Compliance with changing tax regulations	0.78	2	Very important
Security and privacy issues	0.78	2	Very important
Tax-saving opportunities and strategies	0.779	5	Very important
Replacing human expertise	0.75	6	Very important
Accuracy	0.74	7	Somewhat important
Lack of trust	0.68	8	Somewhat important
High cost	0.67	9	Somewhat important
Difficulty to use	0.58	10	Neutral

Table 2 shows that the variable Time reduction has the highest RII value of 0.84, which is interpreted as very important (Aziz et al., 2016) and therefore ranked as 1; this indicates that time reduction is the most perceived benefit of using AI in tax. The second most important variables, all with the same RII value of 0.78, were simplicity, compliance with changing tax regulations, and security and privacy issues, and all three were ranked second. The variable Tax-saving opportunities and strategies ranked fifth in order of importance, with an RII value of 0.779, and the variable Replacing human expertise ranked Sixth with an RII score of 0.75. Notably, all these six variables had a rounded RII value between 0.75 and 0.89 (Aziz et al., 2016), indicating that they should all be interpreted as very important. Accuracy ranked seventh with RII score of 0.74, followed by the variable Lack of Trust with RII score of 0.68 ranking eighth, then High cost ranked nineth with RII score of 0.67, these last three variables are somewhat important (Aziz et al., 2016). The tenth-ranked variable with the importance of neutral was Difficulty to use with RII 0.58.

4.3.2. Correlation Test

The correlation test was conducted to test the below hypotheses:

H12_a: Younger generations are more receptive to the future implementation of AI-driven tax solutions, suggesting a demographic trend in AI technology adoption (alternate hypothesis).

H12₀: There is no demographic trend related to age in AI technology adoption (null hypothesis).

Table 3 shows that Pearson correlation coefficient is r (107) = 0.07, which indicates a very weak to non-existing correlation. Therefore, there is no correlation between age and future implementation of AI-driven tax solutions. So, H12_a will be rejected and H12₀ will be accepted (Table 4).

Table 3. Correlation matrix between age and future implementation of ai in tax compliance and planning.

	Age	Future implementation of AI in tax
Age	1
Future implementation of AI in tax	0.07	1

4.3.3. One Sample T-Test

A two-tailed one-sample t-test was conducted to identify the existence of relation between the eleven identified independent variables and the AI in tax compliance and planning by testing the mean of the eleven independent variables with the neutrality value of the scale μ = 3 because the scale is 1 to 5 in the 5-point Likert scale. Table 5 shows the results of the one-sample t-test; it indicates that the mean scores for ten variables are higher than the midpoint of 3, but for the variable difficulty to use, it is less than 3. The results of the one sample t-test is t (109) > 1.98, p < 0.05, is statistically significant for the following ten variables, sorted by the highest mean to the lowest: Time reduction (M = 4.22, SD = 0.77) which had the highest RII and ranking (refer to Table 2), Simplicity, Compliance with changing tax regulations, Tax-saving opportunities and strategies, Security and privacy issues, Accuracy, Replacing human expertise, Future implementation of AI in tax, High cost and Lack of trust. Therefore, the corresponding null hypotheses are rejected H1₀, H2₀, H3₀, H4₀, H5₀, H6₀, H7₀, H8₀, H9₀ and H10₀ and the corresponding alternative hypotheses are accepted (Table 4). For the variable Difficulty to use (M = 2.88, SD = 0.89), M < μ and t (109) < 1.98, p < 0.05, is not statistically significant, therefore, H11₀ was accepted (Table 4).

Table 4. List of accepted hypotheses.

List of accepted hypotheses

H1a: AI technologies reduce the time required for tax compliance.

H2a: AI increases the accuracy in filing tax returns.

H3a: AI tax software simplifies the tax preparation process.

H4a: AI technologies create concerns regarding security and privacy issues in tax compliance and planning processes than traditional methods.

H5a: AI technologies can replace the expertise of human tax professionals.

H6a: There is a lack of trust in AI’s recommendations in tax compliance and planning.

H7a: AI technologies are too expensive to be used in tax compliance and planning processes than traditional methods.

H8a: AI-powered tax software enhances the identification of tax-saving opportunities, leading to optimized tax strategies.

H9a: AI-powered tax software provides better compliance with changing tax regulations.

H10a: AI-powered tax software will be widely used in the future.

H110: AI-powered tax software is not difficult to use.

H120: Younger generations aren’t more receptive to the future implementation of AI-driven tax solutions, there is no evidence of a demographic trend in AI technology adoption.

Table 5. One sample t-test for the 11 dependent variables.

Variable	Mean	Std dev	t	p-value
Time reduction	4.22	0.77	16.46	6.65E−23
Simplicity	3.92	0.76	12.62	3.90E−13
Compliance with changing tax regulations	3.92	0.82	11.71	4.40E−31
Tax-saving opportunities and strategies	3.90	0.85	11.05	2.10E−19
Security and privacy issues	3.92	0.95	10.04	6.97E−21
Accuracy	3.69	0.87	8.27	1.87E−05
Replacing human expertise	3.75	1.03	7.63	4.10E−17
Future implementation of AI in tax	3.84	1.27	6.94	1.64E−01
High cost	3.36	0.83	4.48	4.23E−05
Lack of trust	3.39	0.94	4.27	1.01E−11
Difficulty to use	2.88	0.89	−1.40	3.21E−10

Below are the corresponding eleven null hypotheses that were tested:

H1_a: AI technologies reduce the time required for tax compliance.

H1₀: AI technologies don’t reduce the time required for tax compliance.

H2_a: AI increases the accuracy in filing tax returns.

H2₀: AI doesn’t increase the accuracy in filing tax returns.

H3_a: AI tax software simplifies the tax preparation process.

H3₀: AI tax software doesn’t simplify the tax preparation process.

H4_a: AI technologies create concerns regarding security and privacy issues in tax compliance and planning processes than traditional methods.

H4₀: AI technologies don’t create concerns regarding security and privacy issues in tax compliance and planning processes than traditional methods.

H5_a: AI technologies can replace the expertise of human tax professionals.

H5₀: AI technologies cannot replace the expertise of human tax professionals.

H6_a: There is a lack of trust in AI’s recommendations in tax compliance and planning.

H6₀: There isn’t a lack of trust in AI’s recommendations in tax compliance and planning.

H7_a: AI technologies are too expensive to be used in tax compliance and planning processes than traditional methods.

H7₀: AI technologies aren’t too expensive to be used in tax compliance and planning processes than traditional methods.

H8_a: AI-powered tax software enhances the identification of tax-saving opportunities, leading to optimized tax strategies.

H8₀: AI-powered tax software doesn’t enhance the identification of tax-saving opportunities, leading to optimized tax strategies.

H9_a: AI-powered tax software provides better compliance with changing tax regulations.

H9₀: AI-powered tax software doesn’t provide better compliance with changing tax regulations.

H10_a: AI-powered tax software will be widely used in the future.

H10₀: AI-powered tax software won’t be widely used in the future.

H11_a: AI-powered tax software is difficult to use.

H11₀: AI-powered tax software is not difficult to use.

H12₀: Younger generations aren’t more receptive to the future implementation of AI-driven tax solutions, there is no evidence of a demographic trend in AI technology adoption.

5. Findings and Implications

The findings of this research show that AI in tax compliance and planning is more time-efficient and accurate than traditional methods which confirms previous studies (Belahouaoui & Attak, 2024). This study also agrees with Górski et al. (2024) about the simplification of the tax preparation process. However, the biggest debate these days revolves around the topic of AI replacing human expertise, contrary to the findings of (Davis, 2024), this study suggests that AI can replace human expertise in tax and agrees with numerous studies that mainly focused on fields where tasks are well-defined, data-driven, and require pattern recognition, such as: medicine where Rajpurkar et al. (2017) found that a deep learning algorithm could detect pneumonia in chest X-rays with a similar accuracy level to radiologists; finance where Wilhelmina Afua Addy et al. (2024) highlighted that AI-based trading systems are now often more accurate and consistent in high-frequency trading than humans; legal analysis where Remus & Levy (2015) suggested that in the legal field, AI is used to analyze legal documents, assist with case predictions, and conduct contract reviews, and customer service (Aslam, 2023; Misischia et al., 2022). Moreover, this study agrees with Tushar Ranjan Barik & Priyanka Ranawat (2024) that AI-powered tax software identifies tax savings opportunities, provides better compliance with changing tax regulations, and will most likely be widely used in the future and is not complex to use. However, this study emphasizes the need to tackle trust, security, and privacy concerns regarding AI in tax compliance and planning, and Tushar Ranjan Barik and Priyanka Ranawat (2024) add the important concern of accountability using AI. Interestingly, contrary to the assumption of Lau regarding the demographic trend in technology adoption, this study found that age does not influence AI technology adoption. Perhaps the explanation for this inconsistency lies in the composition of the sample used, which had included students as well as career professionals in its heterogeneous mix. This variety might have mitigated age differences, especially if the demands of the profession or school environments necessitate incessant use of digital tools irrespective of age. The higher exposure of digital technology to most aspects of day-to-day life might also have facilitated the introduction of a kind of digital literacy across ages, which reduced generational differences in AI usage observed in earlier studies. Therefore, this study suggests and agrees with Huang (2018) that a hybrid approach of AI technology with human touch could be a good mix that allows businesses to benefit from the various advantages discussed earlier of AI while maintaining the necessary checks and balances that human oversight provides.

This study offers meaningful insights into several effects of AI implementation on taxes. It is the first to rank these effects in order of importance. Time reduction was ranked first, followed by simplicity, compliance with changing tax regulations and security and privacy issues; all three ranked second, followed by, ranking fifth to 10th respectively, tax-saving opportunities and strategies, replacing human expertise, accuracy, lack of trust, high cost and difficulty to use. This indicates that even though the essential benefits of AI usage in tax are acknowledged, such as time reduction, compliance and simplicity, the concern is also acknowledged regarding the security and privacy issues (ranking second effect to importance), nearly as significant as the benefits and lack of trust (ranking eighth effect in the order of importance). While previous studies have mostly discussed these factors in a general or theoretical way, the use of the Relative Importance Index (RII) offers a clearer and more structured way to measure how important each effect is perceived to be. By grounding the analysis in actual data, this approach brings more clarity and consistency to the findings. It also helps researchers and practitioners better understand which areas deserve the most attention and therefore this study makes a fresh and valuable contribution to the existing body of work.

This research has paved the way for other researchers and interested governmental, educational, or organizational institutions to investigate further and improve the effects of AI in tax planning and preparation because six out of ten identified impacts were found to be very important, and three effects were found to be somewhat crucial as per the RII classification and interpretation.

The outcome of this study provides several important implications: Policymakers should implement AI in tax systems to enhance efficiency and compliance. However, they should find solutions to the problems related to security and privacy concerns by establishing strict regulations and guidelines. Furthermore, future research should focus on improving AI algorithms to enhance accuracy (ranking seventh effect to importance) and tax-savings capabilities (ranking fifth to impact of importance). Moreover, there should be more focus on training tax professionals to effectively use AI tools, which can help overcome difficulties and build trust in AI recommendations. Additionally, increasing awareness about the benefits and capabilities of AI in tax compliance can help gain public trust and acceptance, which can be achieved through educational campaigns and demonstrations of successful AI applications in tax.

6. Conclusion and Recommendations for Future Research

6.1. Summary of Findings

This research contributes significantly to the academic discourse on AI in tax compliance and planning. It underscores the transformative potential of Artificial Intelligence (AI) in tax compliance and planning, with significant advantages in time savings, accuracy, and regulatory compliance. It also identifies challenges, particularly trust, security, and privacy concerns, while confirming that demographic factors, such as age, do not influence AI adoption. By ranking the effects of AI adoption in terms of importance, this research offers a structured perspective, providing a roadmap for prioritizing benefits and addressing limitations.

6.2. Recommendations for Future Research

While this study provides meaningful contributions, a holistic and multidisciplinary approach is needed to address the evolving role of AI in tax compliance and strategy optimization. Future research should explore the following dimensions: Investigate how AI-powered tax solutions can be seamlessly integrated with Enterprise Resource Planning (ERP) systems and other financial tools, enabling comprehensive financial management and alignment with organizational objectives; Evaluate the usability and accessibility of AI tax systems, focusing on user experience across diverse demographics. Research should identify the training needs of tax professionals to enhance their engagement and trust in AI-based recommendations; Examine the customization and scalability of AI tax systems to meet the unique needs of businesses of various sizes and sectors, including multinational organizations with complex regulatory frameworks; Conduct in-depth studies comparing AI-powered tax solutions to traditional human expertise. Such comparisons should focus on efficiency, accuracy, cost-effectiveness, and areas where hybrid approaches may yield optimal results; Explore how AI adoption in tax compliance influences taxpayer behavior, compliance attitudes, and perceptions of fairness. Ethical dimensions, including accountability, data privacy, and equity, must also be a central focus of future investigations; Research the development of adaptive AI algorithms capable of responding to rapidly changing tax laws and economic conditions, ensuring continued compliance and proactive identification of tax risks and opportunities; Documenting detailed case studies of organizations successfully implementing AI in tax systems will provide actionable insights, foster public trust, and illustrate best practices for adoption across diverse industries.

The findings of this study highlight the need for collaborative efforts among researchers, policymakers, and industry leaders to harness AI’s full potential in tax compliance and planning. Future research should bridge the gap between technological innovation and real-world application, ensuring that AI solutions are efficient, secure, and ethically sound. By addressing these challenges and opportunities, AI can transform tax systems into more transparent, equitable, and dynamic tools that support the evolving needs of modern economies.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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