# The Role of Artificial Intelligence in Strategic Decision-Making

Noel Marimira, Babandi Ibrahim Gumel

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► To cite this version:

Noel Marimira, Babandi Ibrahim Gumel. The Role of Artificial Intelligence in Strategic Decision-Making. Asian Journal of Economics, Business and Accounting, 2025, 25 (3), pp.316 - 327. 10.9734/ajeba/2025/v25i31712. hal-04983278

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# The Role of Artificial Intelligence in Strategic Decision-Making

Noel Marimira a,b* and Babandi Ibrahim Gumel a,c

## Authors' contributions

This work was carried out in collaboration among all authors. Author NM designed the study, performed the analysis and wrote the first draft of the research paper. Author BIG reviewed the document, edited grammatical errors, offered guidance and approved the paper. All authors read and approved the final paper.

## Article Information

DOI: https://doi.org/10.9734/ajeba/2025/v25i31712

**Open Peer Review History:**

This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comments, different versions of the manuscript, comments of the editors, etc are available here: https://pr.sdiarticle5.com/review-history/132265

Received: 12/02/2025

Accepted: 06/03/2025

Published: 08/03/2025

## Original Research Article

## ABSTRACT

**Aims:** The aim of the paper was to examine the issues associated with AI in business decision-making, focusing on matters such as bias, competence, the absence of a comprehensible strategy as well as inadequate attention to strategic, legal and explainability factors.

**Study Design:** Qualitative research design.

**Place and Duration of Study:** MFIs in Zimbabwe, between November 2024 to January 2025.

**Methodology:** Purposive sampling was used to select four participants with specialized expertise in research area of this study. Each of the four interviewed participants had a diverse role in the field of artificial intelligence (1 partnership manager, 1 concept manager for analytics and AI, 1 legal consultant and 1 software engineer).

**Results:** The study highlights how AI is revolutionizing strategic decision-making, especially in relation to automation, predictive analysis, and organizational efficiency. Participants brought up a

<footnote>a LIGS University, USA.</footnote>

<footnote>b 20 Colchester Road, Avonlea, Harare, Zimbabwe.</footnote>

<footnote>c Plot 48 Governor Muazu B. Aliyu Street, Area 7, Takur GRA, Dutse, Jigawa State, Nigeria.</footnote>

<footnote>*Corresponding author: E-mail: noelmarimira1968@gmail.com;</footnote>
number of important topics, such as explainability difficulties, gaps in AI knowledge and biases in decision-making. 100 percent of the respondents agreed that AI systems and technologies are more effective as assisting tools for those who make decisions than as completely independent solutions. The report offers solutions to these issues, including developing diverse teams to contribute a range of viewpoints, putting explainable AI systems in place to guarantee transparency, and raising AI literacy throughout enterprises to reduce competence gaps. These contributions are noteworthy because they address the operational and ethical issues that come up when using AI in decision-making while also providing useful advice for companies wishing to use it.

**Conclusion:** The findings offer valuable guidance for companies looking to adopt AI technologies into their decision-making frameworks, assisting them in overcoming existing obstacles in this area. However further research may be required on a larger scale to validate the findings.

Keywords: Artificial intelligence; strategic decision-making; machine learning; automation; business intelligence; cognitive computing; data analytics; decision support systems.

# ABBREVIATIONS

AI: Artificial Intelligence

DL: Deep Learning

DSS: Decision Support Systems

RPA: Robotic Process Automation

# 1. INTRODUCTION

## 1.1 Background to the Study

Artificial intelligence (AI) is a groundbreaking technological advancement that is revolutionizing businesses by facilitating accurate decision-making, minimizing time and costs, and enhancing data gathering. It combines cloud technology, network devices, robotics, computers and digital content generation. AI plays a fundamental role in determining future marketing schemes as companies increasingly leverage AI software to optimize operations, lower expenses, speed up turnaround times and increase productivity. Organizations that have embraced AI-driven marketing solutions are likely to secure a competitive advantage.

Machine learning leverages AI-driven experiences and empirical data to construct and retain knowledge, enhancing problem-solving efficiency by identifying patterns for algorithms (Brynjolfsson & McAfee, 2017). In contrast, deep learning develops neural networks as it learns, requiring human involvement to provide examples that guide the AI in addressing problems as indicated by Davenport, Guha, Grewal and Bressgott (2020). This approach is frequently employed in multi-layered learning systems designed to tackle complex issues.

Integrating AI into decision-making procedures has the potential to revolutionise businesses by increasing productivity, precision and creativity. However, it also presents challenges that need to

be addressed, including data privacy, security concerns, ethical implications, and the impact on the workforce (Chui, Kamalnath & Carthy, 2018). The automation of tasks through AI can alter job roles, making reskilling or upskilling essential for employees (Duan, Edwards & Dwivedi, 2019). Furthermore, ethical considerations regarding the responsible and transparent deployment of AI technologies are vital. As businesses increasingly adopt AI solutions, the decision-making landscape will experience significant changes that will influence the future of various industries.

## 1.2 Problem Statement

The rapid advancement of AI technologies presents a critical challenge for businesses seeking to incorporate these tools into their strategic policymaking processes. Despite the potential advantages of AI in improving analytical capabilities and generating actionable insights from extensive datasets, numerous organizations face challenges in successfully implementing AI-driven solutions that are compatible with their current business models. This research aims to examine the complexities and implications of AI adoption in decision-making, focusing on how it transforms traditional practices, influences marketing strategies, and addresses significant operational challenges, ultimately reshaping the landscape of business growth and innovation.

## 1.3 Conceptual Framework

Data is a vital resource for AI; without data, AI systems could not operate efficiently or provide insightful suggestions. This link emphasises how crucial it is to include sound data practices into company plans in order to improve decision-making capabilities, as seen in Fig. 1.
<figure><img src="image_4.png" /><figcaption>Fig. 1. Conceptual Framework</figcaption></figure>

Source: Authors configuration

1.4 Brief Literature Review

The rise of artificial intelligence has sparked a considerable amount of academic research examining its effects on various aspects of administrative management, especially in the area of strategic decision-making. Scholars such as Brynjolfsson and McAfee (2017) and Davenport et al. (2020) have extensively explored how AI technologies can enhance policymaking processes within organizations. AI has the potential to offer significant insights through its capabilities in analyzing data, predictive modeling and mechanization, which can empower companies to make well-versed strategic decisions.

The incorporation of AI into tactical management has been examined in various studies that evaluate its possible benefits and drawbacks. Research conducted by Chui, Kamalnath and McCarthy (2018) highlights the transformative impact of AI on organizational performance and competitiveness. Leveraging AI tools enables companies to enhance operational efficiency, optimize the allocation of resources, and improve customer engagement, which collectively contributes to a stronger competitive position in the marketplace.

In the contemporary landscape, data mining, big data, and the handling of vast datasets have emerged as pivotal elements in the decision-making process for organizations (Niu et al.,2021). While humans typically draw on their historical experiences to inform their choices, AI

systems utilize vast datasets to extract insights and make predictions (Ma & Sun, 2020). The discrepancy highlights how humans and AI each contribute uniquely to strategic decision-making, emphasizing their complementary roles in the process.

AI modeling effectively aligns client requirements with service delivery by improving particular decision-making processes and significantly reducing both time and costs. AI systems excel in data collection, forecasting, and trend analysis, enabling businesses to predict customer lifetime value accurately (Dwivedi et al., 2021). This ability ultimately results in a decrease in the bounce rates of the system. Through a process known as data mining or opinion mining, AI analyzes vast amounts of data, including web searches for public opinions and sentiments (Kaplan & Haenlein, 2020).

The study conducted by Duan, Edwards and Dwivedi in 2019 aims to illustrate the impact of AI on policymaking processes. It emphasizes how AI can perform wide-ranging evaluations that may either augment or supplant human participation, mostly in the context of integrating as well as engaging AI structures. The research delves into the consequences of revitalized AI-driven vigorous frameworks and offers various suggestions for experts who work with information systems. The paper opens with a synopsis of the historical development of AI, citing articles from reputable international journals. It explores broad facets of AI and the significant trials it presents, together with the
essential cooperation and synchronisation
needed to either enhance or completely replace
human delegates. Additionally, the study
examines how Al can be applied in dynamic
environments during the big data era, providing
twelve recommendations for specialists, but also
contemplating upcoming trends about the
development of Al and how it relates to human
jobs.

These particular studies were included in the literature evaluation for the purpose of addressing potential issues and offering a fair assessment of the wide range of uses of Al in strategic decision-making. The review captures the intricacy of integrating Al into organizational decision-making by referencing both theoretical and empirical studies. The chosen papers present a variety of viewpoints on Al's potential, ranging from enhancing competitiveness and operational efficiency to revolutionizing the governing process. A comparison table that places the suggested method in line with other previous studies is provided to support the literature review. The distinctive contribution of the current study to the larger subject of Al in strategic decision-making is made clearer by this comparison. Important research, their methods, and their main conclusions are highlighted in the Table 1.

With multiple studies demonstrating Al's capacity to boost productivity, increase predictive accuracy, and optimize resource allocation, the literature review emphasizes Al's enormous potential to revolutionize strategic decision-making. Building on the findings of researchers such as Davenport et al. (2020), Duan et al. (2019) and Brynjolfsson and McAfee (2017), this study advances a more thorough comprehension of Al's influence on contemporary corporate strategy. To guarantee a comprehensive examination of Al's potential, difficulties, and ethical issues in decision-making, these studies have been included. Future studies might examine how Al is integrated into different industries in more detail, providing a better understanding of how businesses can successfully apply Al while managing the risks and difficulties involved.

**1.5 Scope**

The study looks at how developments in artificial
intelligence (Al) are affecting the operations and
service delivery of Microfinance Institutions
(MFIs) in Zimbabwe between November 2024

and January 2025. In order to obtain information
about the integration of Al technologies within
MFIs, implementation challenges, regulatory
considerations and the overall efficacy of these
technologies in enhancing financial inclusion and
operational efficiency, the research will interview
four participants in a variety of roles: partnership
manager, concept manager for analytics and Al,
legal consultant, and software engineer. The goal
of this qualitative method is to give a thorough
grasp of how MFIs are now navigating the
changing digital landscape in Zimbabwe.

**1.6 Justification of the Study**

The study holds significant importance in the quickly changing corporate environment of today, where companies progressively rely on data-driven insights to navigate complex environments. As companies face unprecedented challenges and opportunities, understanding how Al can enhance strategic decision-making processes becomes crucial. Integrating Al into decision-making frameworks allows organizations to improve their responsiveness to changes in the market and enhance their competitive advantage.

Furthermore, by addressing the disparity
between theoretical frameworks of decision-
making as well as real-world uses of Al
technology, our study advances the scholarly
conversation. It explores not only the benefits but
also the potential risks and ethical considerations
associated with Al implementation in strategic
contexts. The research highlights best practices
and offers a roadmap for organizations seeking
to leverage Al effectively through the
examination of case studies across various
industries. This study is significant as it provides
valuable guidance for leaders aiming to harness
Al's capabilities while fostering an organizational
culture that embraces innovation and ethical
responsibility.

Especially in the rapidly evolving corporate
context, this study's main strength is its capacity
to close the gap between academic frameworks
and real-world application. We give businesses
looking to incorporate Al into their strategic
decision-making processes a thorough and
practical guidance by covering the benefits,
difficulties, dangers and ethical issues related to
the technology. Our study contributes
significantly to the body of literature by providing
a more comprehensive view of Al's function,
assisting organizations in successfully navigating
both its potential and its challenges.
<table><caption>Table 1. Comparison of proposed approach with other works</caption><thead><tr><td>Study</td><td>Focus</td><td>Methodology</td><td>Key Findings</td><td>Contribution to Current Study</td></tr></thead><tbody><tr><td>Brynjolfsson & McAfee (2017)</td><td>AI's role in enhancing policymaking</td><td>Literature review and case studies</td><td>AI improves decision-making through data analysis and predictive modeling</td><td>Supports the importance of AI in improving decision-making through data</td></tr><tr><td>Davenport et al. (2020)</td><td>AI in organizational management and strategy</td><td>Case studies and qualitative interviews</td><td>AI enhances efficiency and strategic decision-making by optimizing resource allocation</td><td>Emphasizes the practical applications of AI in organizational settings</td></tr><tr><td>Chui et al. (2018)</td><td>AI's impact on operational efficiency and competitiveness</td><td>Empirical analysis and case studies</td><td>AI increases operational efficiency, resource optimization, and customer engagement</td><td>Aligns with the study's focus on improving efficiency in decision-making</td></tr><tr><td>Niu et al. (2021)</td><td>Big data and AI in decision-making</td><td>Data mining and case study analysis</td><td>AI systems enable accurate predictions and real-time decision-making</td><td>Highlights AI's power in processing and utilizing vast datasets for strategic decisions</td></tr><tr><td>Ma & Sun (2020)</td><td>AI in predictive modeling and forecasting</td><td>Quantitative analysis and modeling</td><td>AI predicts customer behavior and market trends more accurately than traditional methods</td><td>Demonstrates AI's predictive capabilities, a key element in the study's focus on decision-making</td></tr><tr><td>Kaplan & Haenlein (2020)</td><td>AI in data collection and trend analysis</td><td>Literature review and secondary data analysis</td><td>AI excels in data collection, sentiment analysis, and trend forecasting</td><td>Reinforces AI's capabilities in trend analysis and customer insights</td></tr><tr><td>Duan et al. (2019)</td><td>AI in policymaking and dynamic decision-making environments</td><td>Qualitative research and expert interviews</td><td>AI can augment or replace human judgment in decision-making, especially in dynamic contexts</td><td>Provides insight into AI's potential for policymaking, relevant to the current study's focus on AI's broader applications</td></tr></tbody></table>

Source: Marimira (2024)
**1.7 Objective of the Study**

This paper's goal is to critically analyse how Al is incorporated into organisational strategic decision-making processes. In addition to addressing the difficulties and moral dilemmas that come with its use, it seeks to recognise and evaluate the chances that Al offers to improve decision-making effectiveness, precision and creativity.

**2. METHODOLOGY**

To achieve the goal of this paper, which was to examine the ways in which Al is incorporated into strategic decision-making of organizations, a qualitative approach was employed. The study employed a combination of primary and secondary data sources.

**2.1 Primary Data**

The study was enhanced by the use of qualitative interviews, which helped to clarify how Al affects corporate decision-making. To better understand people's experiences and find underlying trends, this study design used a qualitative technique in which respondents provided answers in their own words. In contrast to online questionnaires that enable for widespread involvement from a diverse audience, interviews were favored due to their ability to be carried out either face to face or through zoom meeting, which facilitated a closer exchange of information. This technique also provided the interviewer with enhanced authority over the choice of participants who took part in the study with expertise in the research field.

A roadmap for interviews was developed, as outlined in Appendix 1, featuring questions directed at individuals with proficiency in artificial intelligence, specifically concerning its use in making strategic commercial and legal decisions. Participants were selected grounded on their previous or present involvement with Al in professional settings. The questions' design considered the necessity of striking a balance between structure and standardisation to guarantee that the answers would be as useful and instructive as feasible. The four individuals interviewed for this research represented a range of roles within the Al and technology sectors (refer to Table 2).

In this research, the intended audience included four individuals who represented various

positions in the technology and Al industries. Each respondent offered a distinct viewpoint shaped by their specialized experiences, which was essential for comprehending the complex Al's function in making strategic decisions.

**2.2 Secondary Data**

A thorough literature assessment that included books, scholarly papers, previous surveys, data and reports about the application of Al in corporate decision-making served as the foundation for this paper's theoretical chapter and research design. To identify the most correct and pertinent publications and data for this study, a thorough examination of all theoretical developments was carried out. These were then chosen based on their suitability and relevance.

**2.3 Data Analysis**

To gain a deeper understanding of the subject, a qualitative analysis method was employed, which involved conducting ongoing analyses throughout the interview process rather than solely after data collection (Sutton & Austin, 2015; Moser & Korstjens, 2018). According to Tracy (2024), the final text produced from this method comprises a blend of quotes and descriptive text accompanied by interpretations and comments. Limited written notes were transcribed throughout the sessions and the interviews were taped. Consequently, the tapes were later transcribed in order to increase clarity and comprehension, the written material was divided into parts in accordance with the interview guide because the replies lacked clear themes.

**3. RESULTS AND DISCUSSION**

**3.1 Results**

In this section we share the findings from qualitative interviews among four participants who were highly knowledgeable about Al and making informed choices. The data collected demonstrates a sophisticated comprehension of the ways in which Al technologies are now being integrated into organisational strategy, highlighting the benefits and difficulties faced by decision-makers.

**3.1.1 The use of Al**

Respondent A indicated that Al can be utilized to enhance making business decisions across a
<table><caption>Table 2. Target population</caption><thead><tr><td>Respondent</td><td>Role</td><td>Focus Area</td><td>Industry</td><td>Key Contributions</td></tr></thead><tbody><tr><td>Respondent A</td><td>Partnership Manager</td><td>Enhancing AI application in Zimbabwe</td><td>Technology/AI</td><td>Managing partnerships to optimize AI benefits</td></tr><tr><td>Respondent B</td><td>Concept Manager for Analytics and AI</td><td>Tailoring AI offerings for public sector clients</td><td>IT/Public Sector</td><td>Aligning market demands with AI solutions</td></tr><tr><td>Respondent C</td><td>Legal Consultant</td><td>Providing legal insights on technology and AI</td><td>Consulting/Legal</td><td>Engaging in assignments across various companies</td></tr><tr><td>Respondent D</td><td>Software Engineer</td><td>Developing AI tools for different organizations</td><td>IT/System Development</td><td>Consulting on system development and AI tool creation</td></tr></tbody></table>

Source: Marimira, (2024).

<table><caption>Table 3. Findings from interviews on the use of AI</caption><thead><tr><td>Respondent</td><td>Background/Expertise</td><td>Key Insights</td></tr></thead><tbody><tr><td>Respondent A</td><td>General decision-maker across various industries</td><td>Business decision-making may benefit from AI, but many people are unaware of its capabilities.</td></tr><tr><td>Respondent B</td><td>Expert in analytical dimensions of AI</td><td>Increased knowledge of the analytical applications of AI to improve decision-making.</td></tr><tr><td>Respondent C</td><td>Extensive experience in the public sector</td><td>Worked on several AI projects with an emphasis on machine learning applications and insights on their usage in the public sector.</td></tr><tr><td>Respondent D</td><td>Experience in project management and resource assessment</td><td>Took part in initiatives that used AI to prioritise tasks and meet project management objectives.</td></tr></tbody></table>

Source: Marimira, (2024).
<table><caption>Table 4. Various Uses of AI in Strategic Decision Making</caption><thead><tr><td>Use Case</td><td>Description</td><td>Respondent Insights</td></tr></thead><tbody><tr><td>Efficiency Enhancement</td><td>AI systems streamline processes, reducing time and resources needed for decision-making.</td><td>Respondent A and Respondent D emphasize the importance of AI in enhancing efficiency and automation, leading to quicker decision-making and reduced operational costs.</td></tr><tr><td>Predictive Analysis</td><td>AI analyzes historical data to forecast future trends, enabling proactive decision-making.</td><td>Respondent B emphasizes the ability of AI to enable predictive analysis, which helps businesses foresee market fluctuations and modify their strategies accordingly.</td></tr><tr><td>Error Reduction</td><td>AI learns from past mistakes, minimizing the likelihood of repeating them in future decisions.</td><td>Respondent A emphasizes that the capability of AI to learn from its mistakes greatly enhances the precision of business decisions by helping to avoid past errors.</td></tr><tr><td>Objective Outcomes</td><td>AI provides data-driven insights that reduce biases in decision-making processes.</td><td>Respondent C claims that the effective application of AI results in more accurate and objective outcomes, thereby improving the quality of strategic decisions made by organizations.</td></tr><tr><td>Automation of Routine Tasks</td><td>Automating repetitive tasks allows human resources to focus on more strategic initiatives.</td><td>Both Respondent A and Respondent D emphasize that automation through AI allows employees to reclaim valuable time, which can then be redirected towards more strategic and higher-level thinking.</td></tr></tbody></table>

Source: Marimira, (2024).
variety of sectors. However, many leaders in these industries often do not fully grasp the capabilities of AI and how it can improve their decision-making processes. Respondent B, on the other hand, had a deeper understanding of the investigative aspects of AI. Respondent C brought out significant know-how from the public sector, possessing engaged in numerous AI projects that mainly focus on applications of machine learning. Furthermore, Respondent D was involved in initiatives where AI was employed to evaluate necessities as well as resources for prioritizing tasks and managing projects.

### 3.1.2 Al in strategic decision-making

Table 4 presents a summary of different applications of AI in strategic decision-making, derived from insights obtained through interviews with four participants. Each participant offered distinct viewpoints on the impact of AI on business decisions, highlighting factors such as improved efficiency, predictive analytics, reduction of errors, achievement of objective results and mechanization.

## 3.2 Discussion

Respondent A's organization did not engage in direct investments in artificial intelligence but rather worked with associates who invest on their behalf. The AI technologies employed by Respondent A along with its associates were not designed for any particular sector; rather, they included technologies that were widely applicable, such as information-driven systems. The main goal of every project in Respondent A's business was on putting into practice, which leads to difficulties that are mainly associated with rather than technical execution, information transfer, ethical issues and legal compliance. Respondent A articulated a sense of contentment with the organization's strategy regarding the use of AI, emphasizing a robust pride in cultivating a neutral setting where partners' knowledge exchange and AI development are prioritized over sales goals.

Furthermore, 50 percent of the participants emphasized that a major impact of AI was its capacity to improve organizational efficiency by swiftly and accurately processing large volumes of information, enabling businesses to make quicker decisions while lowering operational costs. Respondent B highlighted AI's analytical capabilities, which facilitated predictive analyses

based on historical data trends, allowing businesses to anticipate shifts in their surroundings or the state of the market and modify their plans appropriately. Additionally, the learning ability of AI, as noted by 25 percent of the respondents, was vital for reducing errors in decision-making processes. AI systems can make suggestions to assist avoid similar problems in the future by looking at prior errors.

According to 25 percent of the participants, the appropriate use of AI leads to outcomes that are more factual and objective than those produced by traditional methods, which are often subject to human biases and subjective interpretations. Additionally, 50 percent of the participants emphasized the advantage of automation, as it enabled organizations to assign routine tasks to AI systems, allowing human employees to focus on strategic planning and complex problem-solving. According to 25 percent of the participants, the most significant impact of AI lies in its ability to enhance efficiency and speed, a sentiment echoed by Haenlein and Kaplan (2019) and Kelechi et al.(2020). Conversely, 50 percent of the participants argued that AI's greatest contribution is in predictive analysis, which assists humans in making informed decisions; this perspective aligns with Dwivedi et al. (2021, p. 7) and Zong and Guan (2024), who highlight that predictive capabilities can foster both efficiency and responsible decision-making through AI. Thus, it can be noted that 100 percent of the respondents agrees that AI systems and technologies are more effective as assisting tools for those who make decisions than as completely independent solutions.

Even though most people think that some activities could ultimately be completely automated, human oversight is still necessary for decision-making processes in the current scenario. Because humans are ultimately in charge of making final decisions, AI is best used to provide recommendations to them. This view was supported by Duan et al. (2019, p. 68), who agree that due to the nascent nature of big data within AI frameworks, decision-making technologies should primarily function as support mechanisms for human operators. A total of 50 percent of the participant's voice their worries about the lack of understanding regarding AI usage and its potential to enhance policymaking, a perspective that is supported by the findings of Aung, Wong and Ting (2021). Additionally, 50 percent of the respondents raised issues about the lack of quality information available,
reinforcing the observations made by
Aung et al. (2021, p. 375) in their earlier
research.

Al's capacity to evaluate vast amounts of data raises a number of legal and strategic issues, especially with regard to the openness of data use in decision-making (Duan, Edwards & Dwivedi, 2019). A revolutionary shift in organizational operations and competition in the contemporary corporate environment is represented by the integration of Al into strategic decision-making. The various ways that Al might enhance decision-making creativity, accuracy, and efficiency have all been covered in detail in this study. Al technologies, such as machine learning algorithms, predictive analytics, and natural language processing, offer organizations substantial opportunity to use data-driven insights that enable better informed and timely decisions, according to a thorough study of the literature and case studies.

4. CONCLUSION

It is becoming more and more crucial for
businesses to create a strategy plan for
integrating Al as the technology develops and
finds uses in a variety of industries. Al's capacity
to handle enormous volumes of data presents a
number of legal and strategic issues, especially
with regard to the openness of data use in
decision-making. The analysis's main conclusion
was that artificial intelligence (Al) tools like
natural language processing, machine learning
algorithms and predictive analytics give
companies a big edge in improving accuracy,
efficiency and decision-making. The findings
highlight Al's broad potential to automate
repetitive jobs so that human workers can
concentrate on more significant duties.

The participants highlighted how Al may increase organizational efficiency by accurately and swiftly processing large amounts of data. A total of 50 percent of the participants emphasized how Al can expedite decision-making and lower operating expenses. Additionally, the other 50 percent pointed out, Al's capacity to forecast future patterns from historical data gives businesses important insights to foresee shifts in the market and modify their plans appropriately. Additionally, the study's findings showed that Al's greatest benefit is in its capacity for predictive analysis, as 75 percent of respondents cited its value in promoting more responsible and informed decision-making. Half of the

respondents (50 percent) listed the automation of
repetitive chores as their top advantage, which
supports Al's wider efficiency benefits.
Furthermore, all respondents (100 percent)
concurred that Al supports human decision-
makers, highlighting the continued need for
human judgment in Al-driven procedures.

This study concludes by highlighting the
expanding significance of Al in operational
improvement and organizational decision-
making. However, it also emphasizes how
crucial human control, high-quality data, and
strategic planning are to successfully
utilizing Al technologies. Businesses must
adjust as Al develops further by creating strong
Al integration plans that strike a balance between
creativity and accountability, guaranteeing that Al
will continue to be a useful instrument in
influencing strategic decision-making in the
future.

DISCLAIMER (ARTIFICIAL INTELLIGENCE)

Author(s) hereby declare that NO generative Al
technologies such as Large Language Models
(ChatGPT, COPILOT, etc) and text-to-image
generators have been used during writing or
editing of this manuscript.

ACKNOWLEDGEMENTS

I am forever grateful to my supervisor and co-
author, Dr. Babandi Gumel, for his guidance,
expertise and unwavering support. His
encouragement and timely constructive feedback
had a significant positive impact on shaping my
research and publications. I thank Professor
Professor Zdeno Matta, for his valuable feedback
and assessment of my seminar paper. I would
also want to thank the entire LIGS University
Team that supports the Finance PhD program for
providing a supportive and transformative
environment that fostered my development and
growth as a researcher. I further extend my
heartfelt gratitude to all participants in this
research study. Their willingness to share their
experiences and insights was invaluable to the
success of this research. To my lovely wife, Dr.
Beauty Marimira, and children, Leona, Princess,
Theo, Tapiwanashe, Takunda, and Tawanda, I
thank you for your patience, understanding, and
unwavering support that enabled me to complete
this PhD journey. To my beloved extended family,
dear friends, and esteemed colleagues across
Sub-Saharan Africa, this is a testament to your
support and encouragement.
COMPETING INTERESTS

Authors have declared that no competing interests exist.

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# APPENDIX 1

**Interview Guide:** The questions used to collect primary data:

I. Has your current workplace made any investments in AI technologies? If so, how long has this investment been ongoing? If not, please proceed to the next section.

II. In which department or area of the company are AI technologies being utilized?

III. What challenges did the company encounter during the implementation of AI?

IV. Are you content with the current application of AI within the organization? If yes, what aspects of AI usage contribute to your satisfaction? If no, what improvements do you believe could be made?

V. How do you perceive the impact of AI on business decision-making processes?

VI. Is your current workplace employing AI for making business decisions? If yes, what challenges arise from using AI in decision-making?

VII. In your opinion, is AI better suited as an aid for human decision-makers or as a replacement for them? Please elaborate on your reasoning.

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of the publisher and/or the editor(s). This publisher and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© Copyright (2025): Author(s). The licensee is the journal publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Peer-review history:**

The peer review history for this paper can be accessed here:

https://pr.sdiarticle5.com/review-history/132265