Developing AI Leadership Capacity in Africa: Exploring the Role of Education, Training, and Mentorship Programs

ABSTRACT

The study explores the evolution of artificial intelligence (AI) leadership skills in Africa, emphasizing the need for mentoring, training, and education programs to cultivate a skilled workforce capable of leveraging AI to advance the continent’s socioeconomic goals. African nations face both opportunities and challenges from the rapid development of AI technologies, especially in harnessing their potential to transform various industries across the continent. However, the current state of AI in Africa is marked by a significant skills gap, limited access to advanced training opportunities, and a lack of structured mentoring programs, all of which prevent the continent from fully harnessing the power of AI. Using a combination of surveys and interviews with industry professionals, this study employs a mixed-methods approach to assess existing initiatives aimed at enhancing business leadership in AI on the continent and to identify the most promising ones. Research indicates that African economies could strengthen their AI leadership capabilities and thrive in the global digital marketplace through targeted educational programs and robust mentoring systems. The study further details how countries like South Africa and Kenya have made remarkable progress in AI development through innovative educational approaches that encourage public-private collaborations. In the long term, the study aims to provide policymakers, academic institutions, and business leaders with practical recommendations for establishing a resilient AI ecosystem in Africa that can adapt to the rapid changes prompted by AI advancements.

1. INTRODUCTION

African artificial intelligence (AI) stands at a crossroads: the continent is on the brink of a technological revolution that could yield significant impacts on its economy and society if fully realized. However, challenges such as insufficient funding, a shortage of trained personnel, and inadequate educational initiatives make this vision difficult to achieve. The African Development Bank estimates that by 2030, artificial intelligence (AI) could contribute $1.8 trillion to Africa’s GDP. This potential is particularly evident in sectors such as healthcare, manufacturing, engineering, education, and banking, where technology can enhance efficiency, creativity, and profitability. Initiatives in countries like Nigeria, where startups leverage AI to provide farmers with data-driven insights, demonstrate that AI techniques can optimize crop quality, improve harvest rates, and reduce inefficiencies in agriculture.[1] One notable example in healthcare is the development of AI tools aimed at early diagnosis of diseases like TB in South Africa, as well as the use of drones to deliver emergency medical supplies to remote locations in Ghana.[2] Such tools facilitate improved healthcare delivery and management through more accurate diagnoses. Nevertheless, significant obstacles remain for the continent. For instance, there is a critical shortage of qualified AI experts, and many individuals lack access to high-quality educational programs that would enable them to acquire the leadership skills necessary to lead AI initiatives. Given that the continent has the lowest number of artificial intelligence scholars compared to developed economies,[3] there is an urgent need for targeted educational programs in Africa. Manyika, (2024) argues that the absence of mentoring programs connecting experienced experts with emerging AI leaders restricts knowledge sharing and hands-on experience development.[4] Efforts to build technological hubs and innovation centers that promote collaboration between educational institutions and industry have been initiated in Rwanda and Kenya; however, these initiatives must be expanded and replicated across the continent to create a successful AI ecosystem. With technological skills and a culture of innovation and leadership, African countries need to invest in education, training, and mentoring programs as AI continues to evolve. By fully utilizing AI, Africa can tackle pressing societal challenges, strengthen its economic foundations, and emerge as a significant player in the global digital landscape. This study will evaluate the critical role of education, training, and mentoring in fostering AI leadership capabilities across the continent, aiding industry stakeholders and entrepreneurs in navigating the complex AI landscape and realizing Africa’s full potential.

• The Need for African Business Leaders to Be Equipped with AI Technology Expertise in Today’s Business Terrain

Pinski et al. (2024) posit that the rapid evolution of the global corporate environment has made the integration of artificial intelligence (AI) technology crucial in achieving competitive advantage, promoting innovation, and enhancing operational efficiency.[5] In African nations, where economic growth and social development are vital for national growth, the urgency for corporate leaders to possess expertise in AI technology keeps increasing, considering the current market trends.[6] The continent faces unique challenges, including resource limitations, infrastructural deficiencies, and socio-economic inequalities. The strategic implementation of AI continues to act as a transformative catalyst, driving businesses toward sustainable growth and improving their ability to navigate complex market dynamics. A key reason for African business executives to adopt AI technology is its potential to enhance their work efficiency and decision-making capabilities. However, conventional leadership approaches often rely on intuition and experience, which, while valuable, still fall short in an era characterized by vast data and rapid technological advancement.

A Manyika analysis (2024) indicates that organizations leveraging data-driven insights for decision-making are significantly more likely to outperform their competitors.[7] This makes it even more imperative for African business leaders to harness AI expertise to use data analytics for informed decision-making based on empirical evidence rather than relying on their instincts. Companies utilizing AI algorithms to analyze consumer behavior have been able to tailor their products and services to better meet market demands, ultimately improving customer satisfaction and loyalty, which is key in today’s highly competitive market. Additionally, Aderibigbe et al. (2023) it asserts that AI technology can optimize operations and increase efficiency across various sectors of the African economy.[8] In the agricultural sector, for example, noted to support about 70% of most African nations’ economies, AI-driven solutions that support precision farming and predictive analytics help optimize resource allocation, boost crop yields, and minimize waste. A 2020 study  Huck et al. indicates that AI applications in agriculture in Niger, despite its location in the Sahara, have increased its agrarian production by 20-30%; this notable example is crucial for a continent where agriculture employs a significant portion of the workforce.[9]

Business leaders who understand and implement AI technologies in their operations can elevate their profitability while promoting food security and economic stability within their communities. Considering the financial sector, banks can adopt AI to transform corporate operations by enabling improved risk assessment, fraud detection, and customer service. Banks and financial institutions employing AI algorithms can easily analyze transaction patterns, which can aid them in identifying fraudulent activities in real-time, thereby safeguarding their assets and enhancing client trust.[10] Furthermore, AI-driven chatbots and virtual assistants are revolutionizing customer support by providing immediate assistance and personalized experiences, which are essential for client retention in a competitive landscape. African business leaders skilled in AI technology can leverage these tools to enhance their operational efficiency and deliver greater value to their customers. Another dire reason for equipping African business executives with AI expertise is the ability to foster innovation and entrepreneurship. The African continent boasts a rapidly growing startup ecosystem, marked by the emergence of various innovation clusters and startup SMEs across multiple countries. However, many of these businesses struggle to scale up due to limited access to advanced technologies and skills. By cultivating AI proficiency among these business leaders, Africa can create an environment that encourages innovation and promotes the growth of technology-driven firms. Initiatives like the African Innovation Challenge aim to equip young entrepreneurs with the skills and resources needed to develop AI solutions that address local challenges, including access to healthcare and education.[11]

Business leaders knowledgeable in AI can then mentor and support younger entrepreneurs and nurture a culture of innovation that drives the continent’s economic growth. Moreover, as global markets become increasingly interconnected, African companies must compete with both domestic and foreign entities. To enhance their global competitiveness in this challenging business terrain, Neupane and Sibal, (2021) maintain that African leaders must adopt AI technologies.[12] Firms using AI for supply chain optimization can reduce costs drastically and improve delivery times, making them more attractive to global partners and clients. Additionally, understanding AI can enable African enterprises to comply with international standards and regulations, particularly in sectors like finance and healthcare, where data privacy and security are paramount. By acquiring AI expertise, African business executives can position their organizations as credible players in the global market. Furthermore, ethical considerations in AI implementation must not be overlooked, especially in the African context, where concerns about data privacy, bias, and accountability are paramount. Business leaders with AI expertise are better equipped to address ethical challenges and establish responsible AI practices within their organizations.

Recognizing the importance of data governance and ethical AI ensures that their AI systems are transparent, fair, and accountable, thereby fostering trust among consumers and stakeholders. Tenakwah and Watson, (2025) emphasizes that ethical AI practices are essential for building public trust in AI technology, which is crucial for its widespread adoption and acceptance.[13] Therefore, it is essential for African business executives to possess expertise in AI technology in today’s corporate landscape. As the continent strives to harness AI’s transformative potential for economic development and social progress, leaders must embrace data-driven decision-making, operational efficiency, innovation, and ethical considerations. Investing in AI education and training will empower African business leaders to enhance the competitiveness of their organizations while contributing to the broader goal of sustainable development across the continent. As AI reshapes industries and redefines the future of work, the ability to navigate this landscape will be a hallmark of effective leadership in Africa. Ultimately, equipping business executives with AI proficiency is not just a strategic necessity; it is a vital step toward realizing the continent’s full potential in the global economy.

• Reviewing Global Trends in AI Education and Leadership: The African Context

The African context provides a distinctive environment for the advancement of AI initiatives, particularly within the realms of education, training, and mentorship. Current literature underscores the need for an increasing recognition of the necessity for localized AI solutions designed to address the specific challenges encountered by communities across the continent.[14] Before the emergence of artificial intelligence, leadership roles in Africa were mostly defined by conventional decision-making methods that depended significantly on leaders’ personal experience, intuition, and administrative hierarchies.

Neupane and Sibal (2021) posits that leaders frequently base their judgments on data from the past and personal judgment, with little accessibility to real-time data or analytical tools.[15] This method, although useful in specific situations, frequently led to inefficiencies and overlooked potential prospects, especially in trending opportunities within the market. In areas like agriculture, African leaders depended on seasonal variations, trends, and personal experience to inform their strategy, occasionally resulting in inefficiency in the allocation of scarce resources and production levels.[16] Indeed, the deficiency in data-driven decisions resulted in numerous African enterprises functioning impulsively instead of proactively, constraining their capacity to come up with creative solutions to solving the continent’s socioeconomic problems.

By leveraging local talent and expertise, stakeholders are more positioned to develop innovative educational initiatives that not only enhance the continent’s digital literacy but also promote sustainable economic growth. These initiatives have the potential to empower the forthcoming generation of leaders and technologists, promoting a sense of ownership over technological progress. Furthermore, they can cultivate a collaborative ecosystem where knowledge sharing leads to the development of customized AI applications, ultimately benefiting diverse sectors such as healthcare, agriculture, and finance. The capacity of AI to stimulate economic expansion and societal transformation across the continent is evident; however, significant challenges remain in realizing this potential. A report by the African Union (2020) underscores the imperative for African nations to engage in AI education and mentorship initiatives to develop a skilled workforce proficient in utilizing AI technologies for development.[17] Various projects have emerged throughout the continent, aiming to bridge the skills gap and enhance AI literacy among younger generations.

However, in recent times, the integration of AI technology has significantly transformed leadership roles in Africa, offering business executives and entrepreneurs opportunities and challenges. One important benefit of AI deployment by today’s leaders is its capacity to improve its decision-making processes by utilizing data analytics and forecasting models. A notable example is Twiga Foods in Kenya, which utilizes AI to enhance its supply chain logistics, allowing the company administrators to make informed decisions about their supply management and delivery by utilizing real-time data.[18] The transition to data-driven leadership will enable African business leaders to adapt more adeptly to trending market needs while responding to the dynamic demands of consumers and cultivating an environment that spurs creativity.

The adoption of AI into contemporary leadership practices still poses numerous issues that African leaders must address. A notable challenge is the widening skills gap prevalent in the labor market. A significant number of corporate leaders and entrepreneurs lack the requisite technical proficiency to incorporate and manage AI technologies successfully. Huck et al. (2020) reports a significant deficit of experienced people in AI and data science throughout the continent, hindering organizations’ capacity to effectively utilize new technologies.[19] The skills gap is especially evident in remote areas, where the unavailability of infrastructural resources and manpower hampers accessibility to high-quality education and training initiatives. Consequently, leaders operating in these areas constantly encounter drawbacks in maintaining a market edge, especially when it comes to competing with businesses that have effectively incorporated AI into their day-to-day activities.[20]

Furthermore, the ethical ramifications of AI implementation present further obstacles for African leaders. As AI systems continue to gain prominence, apprehensions around data protection, algorithmic error, and ethical usage are rapidly emerging. In South Africa, the implementation of AI in automated facial recognition has prompted substantial ethical concerns about confidentiality rights and possibly led to sexism and racial prejudice.[21] African leaders must confront these ethical concerns while guaranteeing that their businesses comply with acceptable AI policies. This necessitates not only technical expertise but also a profound comprehension of the ethical ramifications of using AI technologies.

The significance of AI education, training, and mentorship is paramount in tackling the aforementioned issues and providing adequate training for African leaders to be equipped with the requisite skills and expertise to manage the intricacies of implementing AI. Maina and Kuria (2024) have acknowledged that recently, educational institutions throughout the continent are increasing the significance of incorporating AI into their curricula.[22] As such, the African Institute for Mathematical Sciences (AIMS) provides specialized programs in data science and artificial intelligence designed to grow an emerging generation of leaders capable of leveraging AI technologies aimed at advancing the continent’s socioeconomic growth.[23] Furthermore, mentoring initiatives that link young leaders with seasoned professionals in the AI sector can offer significant information and direction, promoting a work environment of perpetual learning and innovation.

Despite the fact that there is a growing collaboration between the public and private sectors in developing some feasible strategies to augment AI education and training in Africa, partnerships among academia, government entities, and business sector organizations can be enhanced to enable the creation of adapted training workshops that meet the distinct requirements of the continent’s teeming local workforce. Again, the collaboration between Google and multiple African universities seeks to deliver education and hands-on training in the use of AI and machine learning, thereby preparing African students with the necessary skills and know-how for success in this digital era.²³ These efforts will not only augment the technical competencies of prospective leaders but also foster collaborative knowledge exchange across industries in the subregion. By emphasizing AI education, training, and mentorship, African leaders will be able to acquire the needed skills to adeptly navigate the dynamic AI landscape, hence cultivating a more viable and sustainable business climate.

• Assessing the Gaps in Current Literature

Despite the expanding literature on AI projects in Africa, significant gaps persist that necessitate deeper exploration to improve comprehension of AI leadership potential on the continent. A vital area for investigation is the enduring influence of current educational and mentorship initiatives on career paths and leadership advancement among AI practitioners. Despite the fact that many projects have been launched, there is still a lack of real-world data that shows how well they are at producing talented leaders who can drive AI innovation and implementation across all areas of the continent.[24] Furthermore, it is essential to examine the specific challenges encountered by the continent’s marginalized groups, especially rural dwellers and women, in obtaining AI education and mentorship opportunities. Gender gaps in these jurisdiction areas are well-established, and comprehending the hurdles that impede women’s involvement in AI efforts is essential for formulating tailored interventions that encourage diversity and inclusion within the AI space.

Moreover, there is a necessity for a deeper study that examines the influence of government policies on the development of AI education and training structures in various African nations. Examining the impact of legislative actions on the advancement of AI leadership capacity will yield vital information for policymakers and stakeholders at developing strategic policies conducive to promoting AI innovation across the continent. Ultimately, the convergence of artificial intelligence and ethical problems within the African environment necessitates additional investigation. Understanding the ethical and social effects of AI is important for developing responsible management practices as AI technologies spread to more areas. Addressing these ethical confrontations related to AI adoption in Africa will then drive the formulation of standards and guidelines that support ethical AI utilization, promoting equitable distribution of AI benefits throughout Africa.

2. Methodology

Utilizing Ghana’s special status as a center for African diplomatic missions, the data-gathering process for this study took a measured approach. According to Saunders et al. (2019), the choice of data sources ought to be directed by the objectives of the study and the capacity for comprehensive, relevant data.[25] Following this premise, the study concentrated on educational institutions, organizations, and individuals engaged in AI implementation, reached through the embassies and consulates of African nations in Ghana. The principal methods of data collection comprised semi-structured interviews, questionnaires, and document analysis. Creswell & Creswell (2018) assert that a mixed-methods approach facilitates a more thorough comprehension of multifaceted phenomena.[26] This comprehensive approach facilitated the acquisition of both quantitative and qualitative data, offering a detailed perspective on AI leadership growth throughout the continent.

Semi-structured interviews were performed with principal key voices from 30 African embassies and consulates in Ghana. The respondents interviewed comprised education attachés, technology officers, and cultural liaisons who know their countries’ AI initiatives and leadership development programs. The interviews, each lasting around 60 to 90 minutes, were performed either in person or remotely, based on the respondent’s choices and convenience. Kaillio et al. (2016) assert that semi-structured interviews provide flexibility while preserving focus on the research objectives.[27] The interview guide was developed to investigate issues, including contemporary AI education initiatives, obstacles in AI deployment, and methodologies for building AI leadership capabilities. Surveys were disseminated to a larger sample of 150 persons selected through embassy networks, comprising educators, business leaders, and policymakers engaged in AI initiatives around Africa. The poll, conducted online using the Qualtrics platform, included both closed-ended and open-ended questions. Bell et al. (2018) posit that online surveys can conveniently reach a geographically dispersed population more effectively.[28]

The study buying into this assertion took advantage of this medium to examine opinions of AI’s influence on leadership, accessibility to AI education and training, and obstacles to AI adoption across diverse industries from a varied view of respondents across Africa. The analysis of documents constituted an essential element of the data-gathering process. These documents included policy documents, official documents, and program details involving AI projects and leadership development that were acquired from various embassies and consulates. Moreover, publicly accessible materials from African educational institutions and organizations engaged in AI were examined. Kaillio et al. (2016) assert that document analysis is especially beneficial for offering foundational context and augmenting data from alternative sources.[29]

The study included embassies and consulates from all five regions of Africa, North, East, West, Central, and Southern, in its stratified sample to guarantee a thorough representation of the continent. Teddlie and Yu (2019) propose that this stratification facilitates the capture of differences across diverse geographical and cultural contexts.[30] The data collection procedure encountered several challenges, such as disparate levels of AI advancement among nations and possible biases in self-reported information. To address these problems, the study utilized data triangulation and participant verification to minimize such biases.

3. Findings

3.1 Analyzing Leadership AI Development Through Educational Initiatives

The analysis of contemporary educational programs centered on AI in Africa uncovers a developing array of initiatives, however, marked by considerable variations among countries and institutions. The quantitative analysis, derived from data gathered involving 150 educational institutions across 30 African nations, explains the association between diverse factors (Program duration, Industry partnership, Practical projects, and Program effectiveness) on the efficacy of AI educational programs using correlation matrix analysis.

Table 1: Correlation Matrix of AI Educational Initiatives

Note: Correlations are significant at p < 0.01

Strong positive correlations between program efficacy and variables, including faculty expertise (r = 0.75), industry partnerships (r = 0.79), practical projects (r = 0.83), and program duration (r = 0.76), are seen in the correlation matrix. According to these results, AI abilities are typically developed more successfully by lengthier, more realistically oriented programs with knowledgeable instructors and solid industry ties. A multivariate regression analysis was performed to further examine these associations in more detail:

Table 2: Multiple Regression Analysis for AI Educational Initiative Effectiveness

R² = 0.78, Adjusted R² =0.77, F(4, 145) = 128.25,  p < .001

R2 = 0.78 indicates that 78% of the variation in program effectiveness can be explained by the regression model. All factors significantly influence the model, with practical projects exerting the most substantial effect (β = 0.35, p < .001). The quantitative findings support these qualitative data. An education attaché from a West African embassy remarked,

“Programs that incorporate practical projects and industry internships have proven most effective in cultivating job-ready AI professionals.”

This viewpoint was reiterated in other interviews, emphasizing the significance of pragmatic, industry-relevant education. The data indicates an upsurge of advanced AI educational programs in nations with more established technological infrastructures. South Africa, Kenya, and Nigeria comprise 62% of the recognized extensive AI degree programs. Opesemowo and Adekomaya, (2024) noted that the geographical distribution of AI educational resources in Africa is unbalanced, with select countries becoming continental hotspots.

Nevertheless, new strategies are being developed to reduce this inequality. The African Institute for Mathematical Sciences (AIMS), through its African Master’s in Machine Intelligence program, embodies a pan-African effort to democratize opportunities for superior AI education. According to a program coordinator,

“Our objective is to establish a network of AI expertise throughout the continent, rather than solely in conventional technology centers.”

Since, lately, virtual and mixed forms of learning are increasingly gaining prominence within the continent, Coursera and edX have partnered with selected African colleges to provide AI and machine learning courses, enhancing accessibility to AI education within the subregion. Aderibigbe et al. (2023) emphasize that although online platforms enhance accessibility, problems such as internet connectivity and the necessity for practical experience persist in numerous African countries.[31]

The findings showcase that the variety of AI educational programs in Africa is constantly expanding, characterized by a shift towards pragmatic solutions to enhancing the continent’s AI expertise needs with industry-aligned curricula. Nonetheless, considerable effort is still required to enhance access and guarantee that these educational initiatives result in substantial AI leadership capabilities throughout the continent.[32]

3.2 Analyzing Leadership AI Expertise Development               Through Training Opportunities

The analysis of training options in Africa for professionals looking to advance their AI abilities indicates a complicated environment with a range of options and differing accessibility levels. The study analysis looked at the connection between various training options and the growth of capable AI leadership skills. To examine this association, a multiple regression analysis incorporating data from 200 professionals who had engaged in diverse AI training programs around Africa was utilized. The dependent variable, Competent AI Leadership Expertise, was assessed using an aggregate score that took into account technical proficiency, strategic knowledge, and real-world AI application. The independent variables included Education (formal degree programs), Training (short-term courses and workshops), and Mentoring (individual or group mentorship programs).

Table 3: Multiple Regression Analysis for Competent AI Leadership Expertise

R² = 0.68, Adjusted R² =0.67, F(3, 196) = 139.16,  p < .001

The regression model accounts for 68% of the variance in Competent AI Leadership Expertise (R² = 0.68). All three predictors significantly influence the model, with Training exerting the most substantial effect (β = 0.41, p < .001), followed by Education (β = 0.32, p < .001) and Mentoring (β = 0.28, p < .001). The Variance Inflation Factor (VIF) values demonstrate an absence of significant multicollinearity among the predictors.

Once again, qualitative data corroborates these quantitative findings. As a technology officer from an East African embassy stated,

“Short-term, intensive training programs have proven particularly effective in enhancing the skills of professionals already in the workforce. They offer targeted, practical expertise that can be immediately applied.”

The significant influence of Training corresponds with the increasing prevalence of training programs and specialized courses provided by both domestic and international entities. The African AI Accelerator program, a collaboration between Google and the African Institute for Mathematical Sciences (AIMS), offers advanced instruction in machine learning and related applications. A program participant remarked,

“The practical aspect of the training, coupled with engagement in actual AI projects, significantly enhanced my leadership skills in AI implementation.”

Education, although still important, has a slightly reduced influence compared to training. This demonstrates the dynamic evolution of AI technologies and the necessity for continuing education beyond traditional degree programs. Travaly and Muvunyi (2020) assert that the swift advancement of AI requires adaptable, continuous educational opportunities that can promptly respond to technological shifts.[33]

The significance of mentoring, albeit exhibiting the lowest coefficient, according to the researcher, should not be overlooked. Qualitative data indicate that mentorship is essential for cultivating the interpersonal skills required for AI leadership. A mentor at a South African AI incubator program remarked,

“While technical skills are essential, understanding the ethical and strategic ramifications of AI necessitates guidance from seasoned professionals, which is crucial for a newbie to be on top of his game.”

The findings also uncovered inequities in access to these training programs. Urban centers with developed technological infrastructure, including Nairobi, Lagos, and Cape Town, provided a broader array of training opportunities. Thankfully, initiatives such as the IBM Digital Nation Africa program aim to close this gap by offering accessible online AI training throughout the continent. The study found multiple novel methodologies for AI training. The African Development Bank’s Coding for Employment initiative incorporates AI and machine learning components into its digital skills training, benefiting youngsters in many African nations. [34]A program coordinator stated,

“By integrating AI competencies into comprehensive digital literacy programs, we are equipping the forthcoming generation of African leaders to utilize AI across diverse sectors.”

Although traditional schooling is significant, analysis indicates that a blend of specialized training programs and mentorship opportunities is essential for cultivating proficient AI leadership skills in Africa. This is supported by Mahouachi (2025) findings that contend that the future of AI leadership in Africa relies on the establishment of a diversified system of learning opportunities capable of swiftly adapting to technological progress and local requirements. The difficulty resides in expanding these opportunities and guaranteeing equitable access throughout the continent.

3.3 Analyzing Leadership AI Expertise Development Through Mentorship Programs

The analysis of mentorship programs promoting the emergence of AI leaders in Africa indicates an increasing acknowledgment of their significance, while implementation and success levels differ throughout the continent. By applying the regression analysis from Table 3, one can further assess the influence of mentorship on developing proficient AI leadership capabilities. The regression model, mentoring, demonstrated a substantial positive correlation with Competent AI Leadership Expertise (β = 0.28, p < .001). Although this coefficient is weaker than those for Education and Training, it still signifies a considerable effect. To expand on this connection, a hierarchical regression analysis incorporating Mentoring into a model that initially comprised only Education and Training was initiated.

Table 4: Hierarchical Regression Analysis for Competent AI Leadership Expertise

The incorporation of Mentoring into the model led to a notable improvement in R² (ΔR² = 0.07, p < .001), signifying that mentorship initiatives account for an extra 7% of the variance in Competent AI Leadership Expertise, beyond the contributions of Education and Training alone.

These quantitative results are supported by qualitative data, which emphasize the springing forth of diverse mentorship programs throughout Africa. The Deep Learning Indaba mentorship program, which connects young AI researchers with seasoned specialists, has proven to be especially influential. According to a mentee from Nigeria,

“The guidance I received through the Indaba mentorship was invaluable. It not only improved my technical abilities but also assisted me in navigating the intricacies of AI research within the African setting.”

The Women in Machine Learning and Data Science (WiMLDS) mentorship program is a major initiative aimed at assisting women in AI throughout Africa. A program coordinator remarked,

“Our mentorship pairs have led to enhanced research productivity, successful grant submissions, and career progression for numerous mentees.”

The qualitative findings indicate that successful mentorship programs frequently integrate individualized coaching with peer learning opportunities. The AI4D Africa scholarships incorporate a mentorship element that links scholars from several African nations. One participant said,

“The peer mentorship component enabled us to learn from one another’s experiences and establish a support network that persists beyond the program’s duration.”

However, scaling these mentorship programs continues to present difficulties. Nzama et al. (2024) observes that the scarcity of seasoned AI professionals in Africa constrains the pool of possible mentors. In response, certain projects are utilizing global collaborations. The African Institute for Mathematical Sciences (AIMS), for instance, has formed mentorship partnerships with international technology firms, offering African AI students insight into industry best practices as a step in mitigating this challenge.

Whereas mentorship programs demonstrate a substantial positive influence on cultivating AI leadership skills in Africa, opportunities for enhancement remain. Tenakwah and Watson (2025) assert that expanding and formalizing mentorship opportunities will be essential for cultivating the next generation of AI leaders in Africa. The findings depict that challenges continue to remain in developing mentorship models that are adaptable and sustainable so they can reach more prospective AI experts throughout the continent. Furthermore, these desks serve as solutions for enterprises, offering support in navigating regulatory frameworks, resolving trade conflicts, and overcoming bureaucratic obstacles. This role is crucial in the African context, where regulatory inconsistencies and complex bureaucracies significantly hinder cross-border trading. The efficacy of diplomatic economic desks then fluctuates considerably, affected by conditions such as resource distribution, staff skills, and the emphasis placed on economic diplomacy within broader foreign policy priorities.

4. Implications for Policy and Practice

This study’s findings have significant relevance to policymakers and educational leaders aiming to improve AI leadership capacity in Africa, highlighting the necessity for a fundamental change in approach and strategy. The strong positive correlations identified in the quantitative analysis between program effectiveness and factors such as industry partnerships (r = 0.79) and practical projects (r = 0.83) indicate that policies ought to emphasize the incorporation of industry collaboration and practical experience in AI education curricula, transcending conventional classroom-based instructional models. This requires the creation of strong frameworks that encourage and support collaborations between academia and industry (Shiohira, 2021). This can possibly be achieved through using tax incentives for businesses involved in AI education initiatives and for those seeking funding for collaborative research projects. Pinski et al. (2024) confirms this notion by advocating for closing the divide between academia and industry to guarantee AI education aligns with market demands.

The notable impact of Training (β = 0.41, p < .001) on Competent AI Leadership Expertise, as indicated by the regression model, underscores the urgent requirement for policies that promote the establishment of adaptable, short-term training programs, which are essential due to the swiftly changing landscape of AI technologies and the imperative for ongoing learning opportunities. This can end up in the establishment of a country-specific AI skills framework that acknowledges and certifies diverse AI training modalities. Including virtual classes and training programs, thereby offering a standardized yet adaptable method for AI education and professional advancement within the continent. The significance of Mentoring (β = 0.28, p < .001) highlights the necessity for policies that promote mentorship in organizations and throughout the AI ecosystem. This is consistent with Neupane and Sibal (2021) the claim that the idea that mentorship is a crucial element of leadership development in Africa.

Policy initiatives can involve requirements for mentorship programs in publicly financed AI projects through the formation of national AI mentorship collaborations countrywide, fostering a conducive climate for nurturing future AI leaders. The findings highlight the urgency for academia and policymakers to reform curricula to emphasize practical skills and industry relevance, as evidenced by the strong correlation between practical projects and program effectiveness (r = 0.83). This indicates that project-based learning ought to be fundamental to AI education. This keenly necessitates a thorough reevaluation of educational methodologies, including incorporating capstone projects in partnership with local technology firms, as effectively executed by certain universities in the study. The implications necessitate the development of more flexible and adaptable educational models that can align with the swift innovations in AI (Sim, 2019). This can be achieved through expandable curricula that support industry engagement, ensuring that AI education continues to be innovative and pertinent to the evolving demands of the continent.

5. Challenges Confronting Effective AI Leadership Development in Africa

The implementation of effective AI programs for leadership development in Africa continues to encounter numerous intricate difficulties and impediments. Despite the positive implications of the study findings, requiring a thorough understanding of the diverse constraints that must be addressed. A fundamental and widespread concern is the pronounced inequality in resource allocation and opportunity throughout the continent. As data indicates that 62% of extensive AI degree programs are concentrated in only three nations: South Africa, Kenya, and Nigeria. Amankwah-Amoah and Lu, (2024) astutely note that this concentration of resources could compound existing disparities in technological development throughout Africa. Increasing the disparity between AI-ready countries and those that are lagging, with serious consequences for socioeconomic growth and technological innovations across the continent.

The issue of disparities in resource allocation is worsened by substantial infrastructure deficiencies. Especially prevalent in suburban and deprived regions, where inconsistent internet access and insufficient computing capacity critically impede the provision of proficient AI courses and the execution of innovative research. The digital gap, as emphasized by Aderibigbe et al. (2023), persists in affecting the quality and accessibility of AI education throughout Africa, establishing a self-reinforcing cycle of technical disadvantage for specific regions and demographics. A significant obstacle is the severe deficiency of skilled AI instructors and mentors, an issue noticeable across numerous universities attempting to recruit and retain faculty possessing current AI competence. The decrease in the availability of competent human capital phenomenon, wherein outstanding AI talent is frequently enticed by attractive prospects in industry or other universities, presents a substantial danger to the establishment of reliable and excellent AI education programs in Africa. Hence compromising initiatives that nurture local capacity and experience.

The deficiency of skilled workers is intensified by the swift advancement of AI, posing significant hurdles in maintaining up-to-date and pertinent curriculum and training programs. Conventional academic program development cycles frequently fail to match the rapid breakthroughs in AI technologies, requiring more flexible and inventive methods for curriculum design and implementation (Coulson-Thomas, 2023). As a result, the difficulty of synchronizing AI education with region-specific contexts and requirements constitutes a substantial hurdle. This is because several current AI curricula and technologies are formulated in Western contexts and are unlikely to sufficiently tackle the distinct challenges and prospects found in African environments. Moldenhauer and Londt (2018) posit that this discrepancy can lead to a disjunction between AI education and its practical application in addressing local issues, thus constraining the efficacy and pertinence of AI leadership development programs. Again, the ethical and governance concerns associated with AI adoption and utilization present substantial obstacles to effective AI leadership development in Africa. The absence of extensive regulatory structures and ethical standards tailored to the African tailored context could result in ambiguity and possible abuse of AI technologies. This therefore highlights the necessity for AI education programs that prioritize not only gaining technical skills but also principles of ethics to promote responsible AI governance (Lafram & Bahji, 2024).

Furthermore, the issue of gender disparity in AI education and leadership persists in most African countries, especially where culture and tradition do not help with gender disparity. Most often in the subregions, women are frequently marginalized in AI programs and leadership roles, hence constraining the diversity of viewpoints and expertise in the area. Mitigating this gender disparity creates the need for focused support and intervention mechanisms to promote and enhance female involvement in AI education and leadership development programs. These obstacles and barriers highlight the urgency to educate, train, and mentor African leaders to enhance their AI leadership expertise to meet the continent’s dire needs and provide solutions to them. This ultimately requires a comprehensive, multi-stakeholder approach that tackles both educational content and mentorship that suits the wider systemic and societal concerns.

6. Recommendations

Considering the challenges and prospects highlighted in the study, the following broad suggestions are recommended to enhance AI leadership development in Africa:

The creation of a Pan-African AI Education alliance is essential for mitigating the gaps in resources and opportunities throughout the continent. The alliance would encourage innovative collaborations among African universities, research institutes, and industry stakeholders, facilitating the exchange of data, expertise, and best practices in AI education. This partnership would promote the establishment of joint degree programs, collaborative online courses, and faculty exchange efforts, thereby promoting a more cohesive and resilient African AI system. Adewumi and Komolafe (2024) propose that this form of transnational collaboration can alleviate resource imbalances and foster a unified strategy for AI education throughout Africa, thereby utilizing the advantages of current AI centers to promote the growth of nascent ones.

Second, the adoption of adaptable and flexible curricular models is essential for aligning with the swift progress in AI technology. These adaptable curricula ought to be structured for swift updates and customization to incorporate the most recent AI advancements and local situations. This would entail collaborations with industry leaders to jointly develop and perpetually enhance course content, guaranteeing its relevance and application. The combined efforts of micro-credentialing and stacking certificates can offer the requisite agility in AI education, enabling learners to develop their competence following industry demands progressively.

Third, a unified initiative to augment and improve digital infrastructure is crucial for democratizing access to AI education throughout Africa. This recommendation advocates for prioritized investment in broadband connectivity, remote computing resources, and AI-specific technological infrastructure for educational institutions, especially in underprivileged areas. Collaborations with international technology firms should be sought to offer subsidized access to AI platforms and IT infrastructure, guaranteeing that learners and scholars throughout the continent have access to the essential resources for advanced AI education and research.

Additionally, the establishment of AI leadership development initiatives is a vital measure in nurturing comprehensive AI leaders capable of addressing both technical and strategic concerns. These specialized programs ought to integrate comprehensive practical AI training with leadership development, ethics instruction, and strategic thinking competencies. Through collaboration with governmental bodies and private sector entities, these initiatives can offer tangible AI project experiences, thereby closing the gap between theoretical understanding and actual implementation.

Furthermore, building strong industry-academia collaborations is essential for maintaining the significance and efficacy of AI education in Africa. This recommendation pushes for the establishment of organized courses for industry professionals to participate in coaching, mentoring, and conducting joint research with academic institutions. Programs such as corporate awards, hands-on practical initiatives, and collaborative research laboratories can enhance the exchange of information. This will offer students insights into practical AI applications and guarantee that academic research is congruent with industry requirements and regional concerns.

Finally, the creation of a complete AI ethics and regulatory model specifically designed for the African context is crucial for the emergence of responsible AI leadership. This policy framework must direct the incorporation of ethics, policy, and governance components into all AI educational programs, guaranteeing that future AI leaders are prepared to traverse the complex ethical terrain of AI implementation in Africa. The policy structure must tackle concerns including data security, predictive bias, and social consequences of AI technology, emphasizing the distinct ethical considerations pertinent to African communities and economies. Engagement with politicians, moral philosophers, and community leaders in formulating this policy would guarantee its pertinence and efficacy in directing ethical AI development and implementation throughout the continent.

7. Conclusion

The continent’s drive to fully utilize AI technologies has presented a complex mix of opportunities and challenges clarified by this study. The findings highlight the essential importance of pragmatic, industry-relevant education, adaptable training programs, and organized mentorship initiatives in developing proficient AI leaders throughout Africa. The quantitative research demonstrated robust correlations between program effectiveness and characteristics such as business relationships and practical projects, while also underscoring the significant impact of training and mentorship on cultivating AI leadership skills. The study’s recommendations include forming a Pan-African AI education alliance, adopting tailored curriculum models, enhancing digital infrastructure, establishing AI leadership capacity-building initiatives, promoting industry-academia collaborations, and developing an appropriate AI ethics and regulatory model. These offer a strategic blueprint for stakeholders to advance AI leadership development in Africa. By leveraging the continent’s unique advantages and strengths, these suggestions aim to address the identified challenges, including resource inequalities, infrastructure limitations, and the shortage of skilled educators.

As Africa approaches the Fourth Industrial Revolution, cultivating strong AI leadership skills is essential for the continent to actively participate in and influence the global AI landscape. The findings from this study provide critical insights for policymakers, educational institutions, and industry leaders to create an environment conducive to AI innovation and adoption, specifically tailored to Africa’s diverse circumstances and needs. This study has provided an in-depth assessment of the current status and potential trajectories of AI leadership growth in Africa; however, two key areas require further exploration:

The long-term economic impacts of AI leadership development initiatives on African economies, including job creation, sector growth, and technological advancement. The role of indigenous knowledge systems and traditional African leadership models in the development and implementation of AI in Africa. These avenues for further research could yield significant insights into the broader implications of AI leadership development in Africa, thereby contributing to the establishment of distinctly African-centered practices for AI education and deployment. As the field of AI rapidly evolves, ongoing research and adaptation will be crucial for Africa to maintain its leadership in this technological revolution.

References:

1. Aderibigbe, A. O., Ohenhen, P. E., Nwaobia, N. K., Gidiagba, J. O., & Ani, E. C. (2023). Artificial intelligence in developing countries: Bridging the gap between potential and implementation. Computer Science & IT Research Journal, 4(3), 185–199.
2. Neupane, B., & Sibal, P. (2021). Artificial intelligence needs assessment survey in Africa. UNESCO Publishing. https://books.google.com/books?hl=en&lr=&id=Mf4bEAAAQBAJ&oi=fnd&pg=PA4&dq=/+Developing+AI+Leadership+Capacity+in+Africa:+Exploring+the+role+of+education,+training,+and+mentorship+programs+&ots=EApfRI_eM2&sig=grNV4nh3VWvaMsoYTYOcAMDHAnc
3. World Economic Forum. (2020). The future of jobs report 2020. World Economic Forum. https://www.weforum.org/reports/the-future-of-jobs-report-2020
4. Manyika, J. (2024). Creating an AI environment for investments and partnerships in Africa. African Business, 512, 18–19.
5. Pinski, M., Hofmann, T., & Benlian, A. (2024). AI Literacy for the top management: An upper echelons perspective on corporate AI orientation and implementation ability. Electronic Markets, 34(1), 1–23. https://doi.org/10.1007/s12525-024-00707-1
6. Mahouachi, E. (2025). AI [Artificial Intelligence] Fundamentals for Business Leaders. Journal of Values Based Leadership, 18(1), 1–6.
7. Aderibigbe, A. O., Ohenhen, P. E., Nwaobia, N. K., Gidiagba, J. O., & Ani, E. C. (2023). Artificial intelligence in developing countries: Bridging the gap between potential and implementation. Computer Science & IT Research Journal, 4(3), 185–199.
8. Huck, P., Johnson, A., Kiritz, N., & Larson, C. E. (2020). Why AI Governance Matters. RMA Journal, 102(8), 18–24.
9. Coulson-Thomas, C. (2023). AI, Executive and Board Leadership, and Our Collective Future. Effective Executive, 26(3), 5–29.
10. Moldenhauer, L., & Londt, C. (2018). Leadership, Artificial Intelligence and the Need to Redefine Future Skills Development. Proceedings of the European Conference on Management, Leadership & Governance, 155–160.
11. Neupane, B., & Sibal, P. (2021). Artificial intelligence needs assessment survey in Africa. UNESCO Publishing. https://books.google.com/books?hl=en&lr=&id=Mf4bEAAAQBAJ&oi=fnd&pg=PA4&dq=/+Developing+AI+Leadership+Capacity+in+Africa:+Exploring+the+role+of+education,+training,+and+mentorship+programs+&ots=EApfRI_eM2&sig=grNV4nh3VWvaMsoYTYOcAMDHAnc
12. Manyika, J. (2024). Creating an AI environment for investments and partnerships in Africa. African Business, 512, 18–19.
13. Tenakwah, E. S., & Watson, C. (2025). Embracing the AI/automation age: Preparing your workforce for humans and machines working together. Strategy & Leadership, 53(1), 32–48. https://doi.org/10.1108/SL-05-2024-0040
14. Titareva, T., & Skjolsvik, T. (2024). AI Empowered: Re-Defining Leadership for Industry 4.0. Academy of Management Annual Meeting Proceedings, 2024(1), N.PAG-N.PAG. https://doi.org/10.5465/AMPROC.2024.18154abstract
15. Amankwah-Amoah, J., & Lu, Y. (2024). Harnessing AI for business development: A review of drivers and challenges in Africa. Production Planning & Control, 35(13), 1551–1560. https://doi.org/10.1080/09537287.2022.2069049
16. Bischoff, C., Kamoche, K., & Wood, G. (2024). The Formal and Informal Regulation of Labor in AI: The Experience of Eastern and Southern Africa. ILR Review, 77(5), 825–835. https://doi.org/10.1177/00197939241278956c
17. Shiohira, K. (2021). Understanding the Impact of Artificial Intelligence on Skills Development. Education 2030. UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training. https://eric.ed.gov/?id=ED612439
18. (2023). 6 Ways Google is Working With AI in Africa. Business Post Nigeria, 136–567.
19. Kallio, H., Pietilä, A. M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide. Journal of Advanced Nursing, 72(12), 2954-2965.
20. Korstrom, G. (2025). Local companies developing AI. Business in Vancouver, 1839, 4–5.
21. Lafram, A., & Bahji, S. E. (2024). Artificial Intelligence in Morocco: Towards Holistic, Responsible and Ethical National AI Strategy for Moroccan Competitiveness and Strategic Intelligence. Proceedings of the European Conference on Management, Leadership & Governance, 255–263.
22. Lerttanakul, P. & Seongdok Kim. (2018). Enhancing core leadership capacity, organization commitment through leadership building intervention an action research of AAA realty Co., Ltd. UTCC International Journal of Business & Economics, 10(2), 119–137.
23. Maina, A. M., & Kuria, J. (2024). Building an AI Future: Research and Policy Directions for Africa’s Higher Education. 2024 IST-Africa Conference (IST-Africa), 01–09. https://ieeexplore.ieee.org/abstract/document/10569692/
24. Muzuva, M., Zhou, H., & Dumisani Zondo, R. W. (2024). Has generative AI become of age: Assessing its impact on the productivity of SMEs in South Africa. International Journal of Research in Business & Social Science, 13(7), 527–537. https://doi.org/10.20525/ijrbs.v13i7.3576
25. Ndaka, A., Lassou, P. J. C., Kan, K. A. S., & Fosso-Wamba, S. (2024). Toward response-able AI: A decolonial perspective to AI-enabled accounting systems in Africa. Critical Perspectives on Accounting, 99, N.PAG-N.PAG. https://doi.org/10.1016/j.cpa.2024.102736
26. Nzama, M. L., Epizitone, G. A., Moyane, S. P., Nkomo, N., & Mthalane, P. P. (2024). The influence of artificial intelligence on the manufacturing industry in South Africa. South African Journal of Economic & Management Sciences, 27(1), 1–11. https://doi.org/10.4102/sajems.v27i1.5520
27. Okolie, U. C., Nwajiuba, C. A., Binuomote, M. O., Ehiobuche, C., Igu, N. C. N., & Ajoke, O. S. (2020). Career training with mentoring programs in higher education: Facilitating career development and employability of graduates. Education+ Training, 62(3), 214–234.
28. Opesemowo, O. A. G., & Adekomaya, V. (2024). Harnessing artificial intelligence for advancing sustainable development goals in South Africa’s higher education system: A qualitative study. International Journal of Learning, Teaching and Educational Research, 23(3), 67–86.
29. Pinski, M., Hofmann, T., & Benlian, A. (2024). AI Literacy for the top management: An upper echelons perspective on corporate AI orientation and implementation ability. Electronic Markets, 34(1), 1–23. https://doi.org/10.1007/s12525-024-00707-1
30. Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students. Pearson education.
31. Sim, J. H. (2019). Exploring the Relational Leadership Potential of Appreciative Inquiry: A Case Study. South Asian Journal of Business & Management Cases, 8(1), 47–57. https://doi.org/10.1177/2277977918803217
32. Teddlie, C., & Yu, F. (2019). Mixed methods sampling: A typology with examples. Journal of Mixed Methods Research, 1(1), 77-100.
33. Adewum, W. K & Komolafe, O.L. (2024). AI in Africa: Driving change, but at what cost? Capacity Magazine, N.PAG-N.PAG.
34. Lafram, A., & Bahji, S. E. (2024). Artificial Intelligence in Morocco: Towards Holistic, Responsible and Ethical National AI Strategy for Moroccan Competitiveness and Strategic Intelligence. Proceedings of the European Conference on Management, Leadership & Governance, 255–263.

Bibliography:

[1] Aderibigbe, A. O., Ohenhen, P. E., Nwaobia, N. K., Gidiagba, J. O., & Ani, E. C. (2023). Artificial intelligence in developing countries: Bridging the gap between potential and implementation. Computer Science & IT Research Journal, 4(3), 185–199.

[2] Neupane, B., & Sibal, P. (2021). Artificial intelligence needs assessment survey in Africa. UNESCO Publishing. URL

[3] World Economic Forum. (2020). The future of jobs report 2020. World Economic Forum. https://www.weforum.org/reports/the-future-of-jobs-report-2020.

[4] Manyika, J. (2024). Creating an AI environment for investments and partnerships in Africa. African Business, 512, 18–19.

[5] Pinski, M., Hofmann, T., & Benlian, A. (2024). AI Literacy for the top management: An upper echelons perspective on corporate AI orientation and implementation ability. Electronic Markets, 34(1), 1–23. https://doi.org/10.1007/s12525-024-00707-1.

[6] Mahouachi, E. (2025). AI [Artificial Intelligence] Fundamentals for Business Leaders. Journal of Values Based Leadership, 18(1), 1–6.

[7] Aderibigbe, A. O., Ohenhen, P. E., Nwaobia, N. K., Gidiagba, J. O., & Ani, E. C. (2023). Artificial intelligence in developing countries: Bridging the gap between potential and implementation. Computer Science & IT Research Journal, 4(3), 185–199.

[8] Huck, P., Johnson, A., Kiritz, N., & Larson, C. E. (2020). Why AI Governance Matters. RMA Journal, 102(8), 18–24.

[9] Coulson-Thomas, C. (2023). AI, Executive and Board Leadership, and Our Collective Future. Effective Executive, 26(3), 5–29.

[10] Moldenhauer, L., & Londt, C. (2018). Leadership, Artificial Intelligence and the Need to Redefine Future Skills Development. Proceedings of the European Conference on Management, Leadership & Governance, 155–160.

[11] Neupane, B., & Sibal, P. (2021). Artificial intelligence needs assessment survey in Africa. UNESCO Publishing. URL.

[12] Manyika, J. (2024). Creating an AI environment for investments and partnerships in Africa. African Business, 512, 18–19.

[13] Tenakwah, E. S., & Watson, C. (2025). Embracing the AI/automation age: Preparing your workforce for humans and machines working together. Strategy & Leadership, 53(1), 32–48. https://doi.org/10.1108/SL-05-2024-0040.

[14] Titareva, T., & Skjolsvik, T. (2024). AI Empowered: Re-Defining Leadership for Industry 4.0. Academy of Management Annual Meeting Proceedings, 2024(1), N.PAG-N.PAG. https://doi.org/10.5465/AMPROC.2024.18154abstract

[15] Amankwah-Amoah, J., & Lu, Y. (2024). Harnessing AI for business development: A review of drivers and challenges in Africa. Production Planning & Control, 35(13), 1551–1560. https://doi.org/10.1080/09537287.2022.2069049

[16] Bischoff, C., Kamoche, K., & Wood, G. (2024). The Formal and Informal Regulation of Labor in AI: The Experience of Eastern and Southern Africa. ILR Review, 77(5), 825–835. https://doi.org/10.1177/00197939241278956c

[17] Shiohira, K. (2021). Understanding the Impact of Artificial Intelligence on Skills Development. Education 2030. UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training. https://eric.ed.gov/?id=ED612439

[18] Google. (2023). 6 Ways Google is Working With AI in Africa. Business Post Nigeria, 136–567.

[19] Kallio, H., Pietilä, A. M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide. Journal of Advanced Nursing, 72(12), 2954-2965.

[20] Korstrom, G. (2025). Local companies developing AI. Business in Vancouver, 1839, 4–5.

[21] Lafram, A., & Bahji, S. E. (2024). Artificial Intelligence in Morocco: Towards Holistic, Responsible and Ethical National AI Strategy for Moroccan Competitiveness and Strategic Intelligence. Proceedings of the European Conference on Management, Leadership & Governance, 255–263.

[22] Lerttanakul, P. & Seongdok Kim. (2018). Enhancing core leadership capacity, organization commitment through leadership building intervention an action research of AAA realty Co., Ltd. UTCC International Journal of Business & Economics, 10(2), 119–137.

[23] Maina, A. M., & Kuria, J. (2024). Building an AI Future: Research and Policy Directions for Africa’s Higher Education. 2024 IST-Africa Conference (IST-Africa), 01–09. https://ieeexplore.ieee.org/abstract/document/10569692/.

[24] Muzuva, M., Zhou, H., & Dumisani Zondo, R. W. (2024). Has generative AI become of age: Assessing its impact on the productivity of SMEs in South Africa. International Journal of Research in Business & Social Science, 13(7), 527–537. https://doi.org/10.20525/ijrbs.v13i7.3576

[25] Ndaka, A., Lassou, P. J. C., Kan, K. A. S., & Fosso-Wamba, S. (2024). Toward response-able AI: A decolonial perspective to AI-enabled accounting systems in Africa. Critical Perspectives on Accounting, 99, N.PAG-N.PAG. https://doi.org/10.1016/j.cpa.2024.102736

[26] Nzama, M. L., Epizitone, G. A., Moyane, S. P., Nkomo, N., & Mthalane, P. P. (2024). The influence of artificial intelligence on the manufacturing industry in South Africa. South African Journal of Economic & Management Sciences, 27(1), 1–11. https://doi.org/10.4102/sajems.v27i1.5520

[27] Okolie, U. C., Nwajiuba, C. A., Binuomote, M. O., Ehiobuche, C., Igu, N. C. N., & Ajoke, O. S. (2020). Career training with mentoring programs in higher education: Facilitating career development and employability of graduates. Education+ Training, 62(3), 214–234.

[28] Opesemowo, O. A. G., & Adekomaya, V. (2024). Harnessing artificial intelligence for advancing sustainable development goals in South Africa’s higher education system: A qualitative study. International Journal of Learning, Teaching and Educational Research, 23(3), 67–86.

[29] Pinski, M., Hofmann, T., & Benlian, A. (2024). AI Literacy for the top management: An upper echelons perspective on corporate AI orientation and implementation ability. Electronic Markets, 34(1), 1–23. https://doi.org/10.1007/s12525-024-00707-1.

[30] Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students. Pearson education.

[31] Sim, J. H. (2019). Exploring the Relational Leadership Potential of Appreciative Inquiry: A Case Study. South Asian Journal of Business & Management Cases, 8(1), 47–57. https://doi.org/10.1177/2277977918803217

[32] Teddlie, C., & Yu, F. (2019). Mixed methods sampling: A typology with examples. Journal of Mixed Methods Research, 1(1), 77-100.

[33] Adewum, W. K & Komolafe, O.L. (2024). AI in Africa: Driving change, but at what cost? Capacity Magazine, N.PAG-N.PAG.

[34] Lafram, A., & Bahji, S. E. (2024). Artificial Intelligence in Morocco: Towards Holistic, Responsible and Ethical National AI Strategy for Moroccan Competitiveness and Strategic Intelligence. Proceedings of the European Conference on Management, Leadership & Governance, 255–263.