Artificial Intelligence Investment in Resource-Constrained African Economies: Financial, Strategic, and Ethical Trade-Offs with Broader Implications

Abstract

This paper argues that investing in artificial intelligence (AI) in developing economies involves significant trade-offs requiring ethical, financial, and geopolitical scrutiny. While AI is increasingly seen as a vehicle for technological leapfrogging, such ambitions often mask structural constraints, including weak infrastructure, limited institutional capacity, and external dependency. Using the economic theory of opportunity cost—extended through the political economy and digital governance perspectives—this study critically examines AI policy strategies in Ghana, Kenya, and Rwanda. A qualitative design grounded in secondary data and a thematic analysis reveal how AI investment may reallocate scarce resources away from essential services, exacerbate inequality, and entrench strategic technological dependency. This paper proposes a public policy framework built on four principles—sequential readiness, strategic alignment, ethical governance, and capacity building—to guide equitable AI deployment. It argues for establishing a digital social compact between states, citizens, and technology actors to safeguard public interest in AI-driven development. Finally, this paper outlines a future research agenda emphasizing the mixed-method evaluation of AI’s long-term social impacts, including employment, inclusion, and public service delivery.

1. Introduction

Artificial intelligence (AI) has emerged as a focal point for private investment and governmental policy initiatives. Governments worldwide are developing national AI strategies, including those in resource-limited nations across Africa, Southeast Asia, and Latin America. They are establishing innovation hubs and attracting foreign investment to strengthen their position in the global digital economy. These initiatives are premised on the belief that AI can serve as a transformative tool to enable leapfrogging over traditional developmental stages and foster inclusive growth and sustainable development [1,2]. However, in regions that continue to face significant developmental challenges—such as access to clean water, education, healthcare, and energy—a critical question arises: what resources are being reallocated to pursue AI advancements?

Policymakers must determine if AI investments divert essential funding from foundational services, particularly in situations with limited financial resources, low digital literacy, and weak human capital in important public sectors. Investing in advanced digital initiatives, like national AI infrastructures, often requires reallocating funds, potentially reducing budgets for essential public services and infrastructure. This shift can seriously affect long-term economic growth, especially in transitional economies, as human capital and physical infrastructure are crucial for sustainable development [3]. Additionally, government commitment to foreign-backed AI can increase geopolitical dependencies, undermining national digital sovereignty. This is particularly troubling in developing countries, where reliance on imported technology may limit local innovation and dictate digital engagement terms [4].

The opportunity cost of investing in AI spans several dimensions. It includes financial trade-offs in budget allocation, ethical issues related to fairness and employment, and concerns about losing digital sovereignty. A thorough assessment of these trade-offs is crucial to ensure that AI adoption supports long-term national development goals instead of merely facilitating technological advancement that may worsen systemic inequalities [3,5]. This paper will examine how reallocating resources impacts critical public services when policymakers invest heavily in AI. In doing so, the paper aims to contribute a critical perspective to the global conversation on AI and development—one that foregrounds the complexity of prioritization in contexts where every investment decision has profound implications for national welfare and future autonomy.

2. Theoretical Framework and Methodology

2.1. Research Design

This study uses a qualitative, comparative case study design to investigate AI investments’ socioeconomic trade-offs and governance challenges in resource-constrained environments. This approach allows for an analysis of not only policy outcomes but also the underlying assumptions, political dynamics, and institutional capacities that shape AI strategies [6].

The research is conceptual and exploratory, aiming to expand opportunity cost theory [7] by integrating political economy [8,9] and digital governance perspectives [10], particularly in the Global South. Given the rapid changes in AI policymaking and the absence of standardized empirical data in African contexts, a qualitative design is suitable for identifying emerging patterns and structural risks [1,2].

2.2. Case Selection

Cases were selected through purposive sampling based on four specific criteria:

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National AI Strategies: countries with publicly available, government-backed AI policy documents by 2025.

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Economic and Political Diversity: the representation of small (Rwanda), mid-sized (Ghana), and large (Nigeria) economies reflects various development and governance paths.

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Structural Variance: variations in digital infrastructure, human capital, and regulatory capacity for comparative analysis.

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Data Availability: sufficient secondary data, including policy documents and reports, for a solid desk-based analysis.

The selected cases—Rwanda, Ghana, and Nigeria—provide varied insights into the opportunity costs and ethical challenges of AI investment strategies in Sub-Saharan Africa.

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Rwanda is recognized as a leader in technology and aims to be a digital innovation hub in East Africa. Its 2019 state-led AI strategy, backed by international partnerships, offers a significant example of AI investment in a centralized policy framework [11].

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Ghana established a National AI strategy in 2022, aiming to become a regional AI hub by 2030. However, the country faces significant rural infrastructure challenges and ongoing discussions about prioritizing innovation versus basic development needs [12].

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Nigeria, Africa’s largest and most populous economy, is experiencing rapid growth in private-sector-led AI initiatives despite ongoing power, education, and healthcare challenges. This contrast between urban tech growth and systemic development issues makes Nigeria a key case for examining sectoral imbalances and human capital migration [13].

2.3. Data Sources

The analysis is based on the following:

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Official National Policy Documents: national AI strategies and digital transformation blueprints.

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Academic Research: peer-reviewed studies on AI, the political economy, digital ethics, and public investment.

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Development Reports: publications from organizations like the African Union (AU), United Nations (UN), and World Bank, including the Digital Transformation Strategy for Africa (2020–2030) [14].

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Policy Commentary and Think Tank Reports: the analysis of regional implementation challenges.

Using various sources improved the findings’ validity and reduced the dependence on one perspective.

2.4. Analytical Framework and Coding Strategy

This study is founded on the economic principle of opportunity cost, which Lionel Robbins articulated in his 1932 publication, “An Essay on the Nature and Significance of Economic Science”. While this paper uses Lionel Robbins [7] concept of opportunity cost to analyze investments in artificial intelligence, a thorough technology policy analysis also requires considering political economy and digital governance models, especially in the Global South. Robbins’ framework shows that every investment comes with trade-offs; in developing economies, these trade-offs are influenced by global structures that affect available choices and their impacts. Political economy models, such as dependency theory and postcolonial critiques, highlight that technology investment decisions in the Global South often face external pressures [8,9]. International financial institutions, donor agencies, and multinational tech companies shape digital development priorities, affecting which trade-offs are seen as viable. Therefore, the opportunity cost in technology policy is not just an economic calculation but a process influenced by power dynamics, dependencies, and elite negotiations.

The analysis used a deductive thematic coding approach based on opportunity cost, with insights from digital governance and postcolonial political economy [8,10]. The key themes included the following:

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Financial Trade-Offs: resource reallocation and budget impacts in the social sector [15,16].

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Ethical and Social Implications: risks to equity, potential job loss, and issues of distributive justice [17,18].

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Geopolitical Dependencies: dependence on platforms, data sovereignty, and uneven regulations [19,20].

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Readiness and Sequencing: gaps in infrastructure, levels of digital literacy, and institutional capacity [21,22].

Inductive thematic coding was employed to identify key insights, such as human capital migration and strategic technological dependency. Constant comparison techniques helped refine categories and highlight case differences [17].

2.5. Reflexivity of Methodology

The study exclusively uses secondary data sources and does not include primary data collection, such as interviews or surveys. This approach serves the paper’s purpose of developing a critical framework for analyzing AI investment trade-offs and identifying structural patterns, rather than testing hypotheses or making generalizable claims. While this limits the empirical depth, it enhances the theoretical generalizability and facilitates comparative insights across national contexts.

3. Financial Trade-Offs in AI Investment

A fiscal dilemma is faced by developing economies in allocating limited public funds. The prioritization of technologically advanced projects like AI over urgent basic needs is well-documented. In low-income areas, strict budget constraints and dependence on foreign aid often lead to investments in AI initiatives—such as smart cities and research centers—at the expense of critical developmental priorities like healthcare, sanitation, and rural infrastructure [23]. This misallocation poses a significant issue, as seen in Rwanda’s 2019 state-led AI strategy, where substantial government spending occurs even as basic health infrastructure lags in rural regions compared to urban areas [11,24].

In 2022, Rwanda received USD 30 million from the African Development Bank to enhance AI training and smart systems in hospitals [25]. but access to basic healthcare persists in rural areas, where many households lack proper handwashing and sanitation facilities, negatively impacting public health [26]. Inadequate access to water and electricity also contributes to higher maternal and child mortality rates. Therefore, addressing these infrastructure issues in rural clinics is essential for successfully implementing AI and other healthcare improvements, highlighting the opportunity cost of reallocating funds.

When considering financial decisions, it is important to consider the opportunity cost, as defined by Robbins [7] He highlights that using limited resources for one purpose means sacrificing others. For developing economies, investing public funds in costly advanced AI infrastructure raises a critical question: what vital developmental goals are being postponed or ignored? This perspective encourages a careful evaluation of AI investments, especially when funds could be better spent on essential services like healthcare, education, and clean water access.

The financial impact of budget decisions related to AI initiatives goes beyond simple reallocations. Investing public funds in AI often requires substantial long-term commitments to infrastructure, such as costly data storage and foreign consulting services. These resources could instead be used to improve rural clinics, sanitation, or affordable housing [11,16]. Additionally, when funding comes from domestic sources and external donors, local co-financing pressures governments with already limited budgets [15,27]. This can reduce the overall fiscal flexibility, hindering the ability to invest in crucial sectors that directly benefit public welfare.

Such prioritization often channels scarce resources away from foundational needs, producing uneven development outcomes and fiscal stress. For instance, funding AI diagnostics in urban hospitals can detract from vital public health initiatives to reduce child mortality and improve maternal health in rural areas [27]. This focus on visible projects is worsened by the uncertain long-term returns on AI investments, which can fluctuate with global market and geopolitical conditions [11,16]. In contrast, investing in essential public goods like water access, maternal health, and education offers more reliable and equitable benefits that are crucial for reducing poverty and enhancing life expectancy [11,27].

While AI has significant long-term economic potential, prioritizing it in resource-limited settings can distort development priorities. Using scarce public or donor funds for AI initiatives—often motivated by prestige and donor influence—can lead to a misallocation of resources that exacerbates inequalities and undermines the overall development goals [16].

The intricate and interconnected nature of these trade-offs—financial, strategic, and social—can be more comprehensively understood through the opportunity cost framework. As depicted in Figure 1 below, investments in artificial intelligence, while potentially advantageous, necessitate the careful consideration of the aspects being relinquished in the process.

Figure 1 illustrates the fiscal choices governments must make when allocating limited resources. External influences often favor AI investment, which can divert funding from essential public services. The opportunity cost includes delayed social progress and growing inequality when readiness is lacking. This model emphasizes the need to assess internal trade-offs and external factors when considering the opportunity costs of AI investment in the Global South.

4. Ethical and Social Implications

Investing in AI in resource-constrained environments raises ethical concerns regarding access and equity. Research shows that AI investments often benefit urban elites, worsening digital divides and marginalizing rural and low-income populations. This situation limits their participation in the digital economy and exacerbates inequality [17]. Technology should promote inclusive development, not hinder it [17]. Facebook’s Free Basics program, launched in several Sub-Saharan African countries, like Nigeria, Zambia, and Kenya, claims to promote digital inclusion. However, it restricts users to a limited portion of the internet controlled by Facebook, reducing their informational autonomy and creating dependence on a single corporate platform. Critics argue this approach leads to digital colonialism, as it prioritizes corporate interests over open and participatory access to information [10].

Additionally, AI poses the risk of displacing low-skill jobs, especially in African economies where informal and semi-formal work is vital for income. Rodrik [18]. warns that automation may threaten jobs in key manufacturing and service sectors, particularly without sufficient safety nets or retraining programs. The absence of such measures increases vulnerabilities for workers unable to transition into new roles [17].

Simultaneously, the public discourse on AI often aligns with elite narratives, neglecting urgent community needs, such as maternal health, local governance, and food insecurity. As policy attention shifts to futuristic technologies, essential issues remain underfunded, undermining socioeconomic development and highlighting problems of distributive justice [28].

Youth aspirations increasingly center on data science and technology careers, driven by donor-funded innovation programs and partnerships with global tech companies. While innovation is important, this focus can lead to talent shortages in critical sectors like primary education and healthcare [29]. Over time, this talent drain may weaken public institutions and deepen disparities between urban and rural areas, impacting effective governance and service delivery.

The pursuit of AI-led development raises important ethical concerns regarding intergenerational justice and fairness, particularly the potential exploitation of people for data collection. Without inclusive governance and culturally relevant ethical standards, AI deployment may worsen existing vulnerabilities rather than help them [30]. Therefore, a nuanced dialogue about AI ethics is essential, incorporating diverse perspectives and community needs into technological solutions. Prioritizing AI in developing contexts involves complex issues of access, equity, and social justice. While AI technologies offer clear benefits, their integration into societies with significant inequalities needs careful consideration to ensure that all members of society can access the advantages of technological progress.

5. Geopolitical Platform Dependency and Regulatory Influence

In developing economies, investment in AI is significantly influenced by external sources, as much of the funding, infrastructure, and technical expertise originates from powerful states or multinational corporations. This external reliance creates various dependencies that can have long-term geopolitical and developmental consequences. A key concern related to this external dominance is platform dependency. Many developing nations have adopted AI technologies and platforms developed abroad, particularly by companies from China and the United States. These technologies are often proprietary and operate as closed systems, making local adaptation and auditing increasingly challenging. This dependency raises critical issues surrounding data localization, privacy, and control over essential national data, which is frequently stored offshore and susceptible to misuse and surveillance [31]. An exemplary case involves government agencies in Rwanda and Kenya, which consistently utilize Zoom for judicial hearings and cabinet meetings. These activities depend on platforms managed by U.S. corporations, posing risks of foreign surveillance and compromising national communication sovereignty.

Moreover, strategic alignment with foreign powers poses an additional concern. Accepting AI infrastructure and funding from countries like China—through initiatives like the Belt and Road Initiative, including a “Digital Silk Road” featuring AI systems—can compel developing nations to align with foreign geopolitical interests. Such alignment may impose restrictive conditions, potentially impeding a country’s autonomy in foreign policy. For instance, investments in AI from U.S.-based entities often come with normative expectations concerning data governance and intellectual property, which may not align with local stakeholders’ policy priorities or capacities [32].

Imported AI systems often reflect the legal and ethical standards of their countries of origin, which can conflict with local cultural norms and legal frameworks. For instance, Taylor [33]. found that facial recognition algorithms created for Western contexts show racial biases when used in African and Asian populations, raising significant ethical concerns [34]. This external regulatory influence can hinder local governance and decision-making. Moreover, the geopolitical landscape presents a considerable opportunity cost. Rapid AI development in emerging economies may lead to diminished policy autonomy, increased cybersecurity risks, and a form of digital neocolonialism [19,35]. Instead of building a self-reliant innovation ecosystem, these nations risk becoming dependent on foreign AI products and services, limiting their influence over technological advancements and ethical considerations. Developing countries must establish national strategies to address these challenges, promoting open-source development, regional collaboration, and enhanced local capabilities. Without these measures, the disadvantages of AI dependency may overshadow [19,23] its potential benefits [32,36].

Strategic Technological Dependency

The technological dependency of small and medium-sized developing states is concerning, as these nations often become testing grounds for standards set by major powers. Digital platforms and AI innovations tend to reflect external agendas, leaving local communities vulnerable to foreign surveillance and governance rather than addressing local needs [37]. For example, the facial recognition and smart city technologies introduced in East African cities have been adopted with little local consultation or ethical oversight, embedding global surveillance practices into local environments [38]. A notable instance is Huawei’s implementation of smart city systems in Kenya, Uganda, and Zambia, encompassing surveillance networks, facial recognition technologies, and data centers. These technologies function through proprietary infrastructure managed by the Chinese vendor, characterized by limited transparency and insufficient data localization assurances. This situation has generated apprehensions regarding opaque governance, the potential misuse of citizen data, and long-term reliance on external systems [38,39].

The rise of digital financial platforms in the Global South highlights a crucial aspect of technological dependency. These platforms often trap countries in specific data systems and payment frameworks, making switching or leaving these ecosystems costly and complicated [37]. This lock-in effect stifles local innovation and hampers efforts to build and strengthen indigenous digital capabilities [40]. Mobile Money transfer services and M-Pesa’s expansion outside Kenya through partnerships with Visa and global payment networks highlight the platform’s dependency on fintech. While this enhances transaction options, it ties African digital finance to foreign regulations and systems, restricting the region’s ability to develop independent payment ecosystems.

Such a trend restricts smaller states’ policy independence and adaptability, thereby positioning them as mere data providers and consumers within a technological supply chain dominated by major economic powers [41].

The reliance on externally governed digital platforms weakens small and medium-sized states’ ability to create localized innovation ecosystems. This hampers their capacity to address unique socioeconomic challenges and affects vital sectors like financial services, governance, and public safety [42]. As a result, states lose digital sovereignty and struggle to implement inclusive development policies [37]. To counter this, national innovation strategies must focus on building local capacity and developing resilient technological infrastructures [42].

While testing advanced technology in less-regulated settings may yield short-term benefits, it can also deepen structural inequities and create long-term dependencies that threaten digital sovereignty. Academic discussions increasingly advocate for policy actions that promote regulatory oversight, transparency in adopting foreign technologies, and support for indigenous innovation ecosystems [41,43]. These actions are crucial for transforming digital platforms from dependency tools into drivers of equitable socioeconomic development.

6. Case Studies in Misalignment: When AI Overshadows Basic Needs

6.1. AI vs. Basic Infrastructure Needs

The disconnect between high-profile AI initiatives and the persistent underinvestment in essential public infrastructure in many developing economies reflects a significant challenge in achieving inclusive development. For example, in several African nations, government partnerships with global technology firms to implement AI applications in sectors such as agriculture and transportation have attracted international attention and prestige. However, these ambitious projects often run parallel to significant shortages in foundational services, including access to safe drinking water, quality public education, and reliable electricity networks. This situation underscores the opportunity costs associated with heavy investments in digitization and advanced technologies, while essential services, crucial for socioeconomic well-being and equitable growth, remain neglected [44,45].

In 2023, Kenya allocated USD 6 million for the Kenya National AI Strategy (2025–2030) to establish the country as a regional leader in AI across various sectors, including agriculture, which is vital for economic growth [46]. Around the same time, communities like Turkana County face significant challenges, with over 80% of households lacking access to clean water. This situation leads to health issues and high child mortality rates from waterborne diseases [47].

Investing in AI technologies aims to tackle agricultural inefficiencies and transport logistics challenges. However, it is crucial to recognize that many people, particularly in rural areas like Turkana, still struggle with basic needs. Research shows that without clean water, efforts to enhance technology may do little for health and economic growth [48]. Recent studies indicate that AI-driven analytics could transform urban water systems by improving reliability and sustainability. However, these advantages depend on addressing the fundamental deficiencies in public utilities and infrastructure maintenance [24]. These findings suggest that AI’s transformative potential may remain largely unactualized without a comprehensive overhaul of public systems.

Nigeria’s experience exemplifies the challenges associated with government-led initiatives in AI. Despite aspirations to modernize various economic sectors, the persistent deficiencies in the national power grid significantly hinder economic resilience. Electricity, an essential service for local communities and digital economies, suffers from chronic underinvestment. This situation is further complicated by a detrimental cycle in which capital-intensive investments in data centers and AI platforms compete with and often displace necessary improvements in energy delivery systems. Empirical analyses of infrastructure investments in emerging markets indicate that such trade-offs can exacerbate regional inequities, disproportionately benefiting urban digital hubs while limiting broader population access to reliable basic services. A major AI data center in Lagos consumes approximately 1 MW of electricity per day—enough to power nearly 1500 rural homes—while 43% of Nigeria’s population lacked consistent electricity in 2023 [49].

Moreover, the challenges extend beyond electricity and water services. The education sector, often already burdened by overcrowded classrooms and a shortage of qualified teachers, encounters additional difficulties when technology and AI are prioritized without equivalent investments in human capital and foundational educational infrastructure. Case studies indicate that while AI applications have the potential to enhance learning experiences and improve efficiency within education, these initiatives must be integrated into a comprehensive framework that reinforces essential educational services [22]. Such a framework would ensure that technological advancements are not pursued in isolation but as part of a holistic development strategy to enhance overall societal well-being.

The evidence shows that AI and digital innovation have the potential for significant economic transformation but also risk worsening inequalities in many developing countries. Advanced AI applications can deepen disparities if resources are funneled away from essential services like water, electricity, and education. Recent studies have highlighted the need for a balanced strategic approach to ensure that technological advancement does not exacerbate socioeconomic gaps.

6.2. External Pressure to “Leapfrog” vs. Internal Developmental Priorities

The concept of “leapfrogging” in international development suggests that developing countries can skip traditional industrialization by adopting emerging technologies like AI, blockchain, and drones. However, as seen in Ghana’s National AI Strategy, this approach often overlooks local development needs. While these strategies aim to make countries technological hubs by 2030, they typically rely on foreign partnerships, neglecting persistent issues such as limited internet access in rural areas, unreliable electricity, and underfunded technical training [12,50] This creates a scenario where an internationally connected elite benefits from these high-tech investments while many still lack basic digital access and infrastructure. The dependence on imported technologies and global standards can lead to “digital coloniality”, where external donors dictate technological goals. Focusing on advanced technologies rather than community-specific needs can undermine policy sovereignty and lead to development strategies shaped by outside interests rather than local priorities [12,51].

The narrative of leapfrogging can lead to dual economies, where the benefits of technological innovation are concentrated among the already privileged. This issue worsens when emerging technologies are pursued without investing in the necessary technological and social infrastructure for inclusive development [50]. In practice, as seen in Ghana, ignoring basic infrastructure and training needs can deepen existing inequalities and weaken long-term national autonomy in technology policy [12,51]. This broader tension between aspiration and structural readiness necessitates further reflection on the viability of leapfrogging strategies in resource-constrained contexts.

6.3. The Risks of Leapfrogging Without Foundations

Leveraging technological leapfrogging to skip traditional development stages in countries like Ghana, Kenya, and Rwanda faces significant challenges due to structural deficiencies in institutional capacity, infrastructure, and regulatory frameworks. Research shows that while leapfrogging is appealing, weak local governance and foundational systems limit its effectiveness. A systematic review of e-government in developing countries reveals that relying heavily on advanced digital platforms can widen urban–rural gaps by prioritizing services suited for areas with strong institutional support [52]. As a result, the benefits of leapfrogging are often limited to urban regions with better resources and infrastructure. This situation is similar to introducing AI technologies in economies grappling with uneven infrastructure and weak public institutions.

The discussion on artificial intelligence in low-income countries reveals that current AI initiatives often rely on external expertise and donor agendas, rather than aligning with local development priorities [2] this dependence on proprietary platforms risks prioritizing superficial modernization over genuine socioeconomic inclusion. When advanced AI technologies are adopted without adequate investment in local capacity and regulatory oversight, the rapid pace of technological change often outstrips the capability of local institutions, leading to misallocated resources and increased inequalities.

Research on regulatory frameworks highlights these challenges. An analysis of Africa’s AI regulations in healthcare shows that despite growing interest in AI, the lack of robust governance creates vulnerabilities and sustains inequalities [2]. This governance deficiency is prevalent beyond healthcare and reflects broader issues. A comparison of state-building in Ghana and Rwanda reveals that successful technology integration needs a balance between technical expertise and effective governance structures. In both countries, elite networks, or “technopols”, have accelerated technological adoption, yet the institutional framework is inadequate to ensure equitable benefit distribution.

Rwanda’s experience in building a knowledge society through targeted ICT policies serves as both a model and a warning. Strategic ICT investments have transformed parts of its economy [53], but using advanced digital tools without strengthening basic public services reveals significant tensions. International interventions in East Africa have produced mixed results due to institutional constraints and differing national priorities [54]. Evaluations of e-government platforms indicate that the gap between the ambitious narratives of technological leapfrogging and the actual outcomes can lead to superficial results instead of real transformation [55]. It is suggested that a phased, context-sensitive approach should be taken, strengthening foundational systems in health, education, and energy before scaling advanced technologies rapidly.

The evidence shows that while using advanced AI technologies is appealing, it must be implemented carefully in resource-limited areas. A strong institutional and infrastructural foundation is essential before integrating these technologies. Developing economies can achieve sustainable and inclusive growth only by aligning technology with local capacities and needs.

6.4. Human Capital Migration and Sectoral Imbalance

The opportunity cost of investing in AI in developing economies goes beyond financial expenditures; it also affects the distribution of human capital. In Kenya, for instance, universities such as the University of Nairobi and Strathmore University have expanded their AI programs, while teaching and public health enrollments have stagnated. This raises concerns about the potential decline of essential societal capabilities [56,57]. This talent shift is echoed across the region, where the appeal of tech careers increasingly overshadows roles in education and health sectors vital to long-term public welfare.

Similarly, private technology hubs are partnering with foreign companies to recruit top STEM talent in Nigeria, diverting skilled professionals from critical public services sectors like education and healthcare. As a result, the advantages of technology investments are concentrated among a small group, while the broader educational system faces issues such as teacher attrition and recruitment challenges. While AI investments may develop future skills, they risk undermining the talent pool necessary for maintaining vital public infrastructures [13].

In Ghana, the appeal of lucrative international careers in AI has led to an increase in brain drain, resulting in skilled graduates pursuing postgraduate opportunities in Europe and North America. This exodus diminishes the local capacity to deliver essential services in healthcare, education, and other sectors, adversely affecting human development outcomes. The migration of skilled professionals profoundly impacts the availability of local talent, indicating that substantial investment in AI could undermine vital sectors that contribute to societal well-being [58,59].

Although investments in AI stimulate technological advancement, they may also divert critical human resources from sectors that provide long-term societal benefits. If this matter remains unaddressed, it risks undermining the essential public systems necessary for inclusive development, thereby revealing a substantial opportunity cost that policymakers should consider about technological progress. Developing a comprehensive investment strategy that supports technological innovation and fundamental human services is imperative to prevent the exacerbation of systemic inequalities within developing economies.

7. Towards Equitable, Context-Aware AI Investment: A Public Policy Framework

AI investments in developing economies should be part of a comprehensive strategy considering each nation’s unique context. Instead of opposing AI investment, we should focus on four key principles: sequencing, strategic alignment, ethical governance, and capacity building. This approach will ensure that AI deployment contributes to the overall national development goals. The section presents a public policy framework to direct AI investments in resource-constrained economies. It specifies clear stages and decision points to ensure AI deployments foster equitable and sustainable development.

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Sequencing infrastructure and skills readiness assessment: Investments in basic digital infrastructure and human development should come before or alongside the scaling of advanced AI systems. Policy frameworks emphasize this step-by-step approach, ensuring essential technologies like reliable connectivity and strong data management are in place before introducing complex services [21]. This careful progression helps prevent resource misallocation and reduces disparities in digital access, as shown by frameworks that link infrastructure development with AI innovation [60].

Before investing heavily in advanced AI initiatives, governments should conduct a mandatory readiness assessment to evaluate the following:

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  Digital infrastructure maturity (broadband access, electricity reliability).

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  Key human capital indicators (digital literacy rates, STEM education levels).

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  Basic governance and regulatory capacity for technology oversight.

Investments should follow a phased model, advancing to higher levels of AI complexity only when specific readiness benchmarks are met.

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Strategic alignment: AI initiatives should be directly linked to national development priorities to maximize the public benefit. For example, using AI for crop disease prediction or health diagnostics can effectively address local challenges when integrated into a country’s socioeconomic context [61]. A SWOT analysis related to the Sustainable Development Goals shows that aligning AI projects with clear strategic objectives can help nations tackle immediate issues and prepare for future growth [62]. Each major AI project should be embedded within a Strategic Development Integration Plan that explicitly connects it to national development priorities and should include the following:

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Clearly define the problem in relation to the Sustainable Development Goals (SDGs).

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Conduct a cost-benefit analysis, including opportunity costs.

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Assess the social equity impact.

This ensures that AI initiatives are not stand-alone prestige projects but integral to broader, inclusive development strategies.

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Ethical and inclusive governance: Ethical governance is crucial for creating inclusive and localized frameworks for AI deployment. These frameworks should ensure that AI adoption respects human rights and builds public trust through transparency, fairness, and accountability [60]. By empowering local stakeholders to establish the rules for technology use, ethical governance helps mitigate risks like data exploitation and digital coloniality. Research highlights the importance of human agency in implementing AI systems [63]. Governments must establish independent AI Ethics Councils or commissions tasked with:

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Review AI projects for compliance with human rights, fairness, and transparency.

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Conduct consultations with marginalized communities.

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Establish public oversight and accountability for technology deployments.

Governance structures should be protected from foreign donors or corporate influence to ensure AI standards align with local cultural values and societal needs.

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Capacity building as a Core investment Line: Capacity building is essential for enhancing digital literacy and fostering local innovation ecosystems. A strong capacity-building strategy tackles skill gaps by promoting educational initiatives and training programs that enable citizens to engage effectively with AI technologies [64]. This approach reduces reliance on foreign expertise and supports the country’s long-term sustainability of AI projects. By developing local capabilities, capacity building helps create a self-sustaining digital economy and mitigates the social impacts of rapid technological change [21].

All public AI investments must allocate a mandatory percentage of the budget for local capacity building, including the following:

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  Training programs for civil servants, educators, and community leaders.

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  Support for indigenous tech startups and research institutions.

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  Public education initiatives to improve citizen understanding and engagement with AI technologies.

This method integrates technology with local knowledge, decreasing reliance on outside expertise in the long run.

Figure 2 shows a pyramid outlining four essential stages for responsible AI investment. It starts with capacity building to enhance local innovation, followed by establishing ethical governance, aligning with national development goals, and conducting an infrastructure and skills readiness assessment. Each stage must be completed before moving on to more advanced AI systems, promoting inclusive and context-aware digital transformation. This framework serves as a clear guide for policymakers to integrate AI investments into broader development strategies, minimizing opportunity costs and external dependencies.

Developing economies can protect themselves from the risks of hasty digital adoption and over-reliance on external sources by prioritizing investments in human capital, ethical governance, and strategic planning. AI should be used to promote widespread development rather than to increase inequalities. A local and sequential approach to AI deployment is essential for achieving inclusive growth, maintaining national independence, and fostering a resilient digital future.

8. Conclusions

Investing in AI in developing economies involves more than financial considerations; it presents significant political and ethical challenges. Responsible AI deployment requires creating a digital social compact—an explicit and evolving agreement among the state, citizens, and technology actors. This compact should prioritize public interest, focus on inclusive development, and ensure democratic accountability in digital advancements. Without such a framework, AI investments risk worsening inequalities, increasing geopolitical dependencies, and undermining local governance.

The proposed public policy framework highlights the importance of sequential development, ethical oversight, alignment with national priorities, and substantial capacity building for sustainable AI adoption. Furthermore, achieving an equitable digital future necessitates rigorous evaluation mechanisms to analyze the actual impacts on social welfare.

Future research should focus on longitudinal methods to assess the impact of AI investments on access to essential services, employment equity, educational outcomes, and digital inclusion. Using mixed methods—such as econometric analysis, participatory research, and policy ethnographies—can provide deeper insights into how AI affects social contracts in different contexts. Comparative studies across countries with different levels of AI development can help identify best practices and lessons for ensuring technological innovation aligns with developmental justice.

To protect the public value of AI in resource-limited areas, it is essential to commit to democratic governance, social equity, and strong institutions. Building a digital future that benefits everyone, not just a select few, is crucial.

9. Limitations

This study relies on secondary sources, such as policy documents, academic studies, and development reports, without any primary data collection or mixed-method validation. Consequently, it lacks in-depth empirical insights that could have been gained from firsthand interviews, fieldwork, or original surveys related to AI investment decision-making processes at the national and local levels. This approach was intentional, aligning with the study’s goal to analyze the structural trade-offs of AI investment in resource-constrained environments, rather than evaluating a specific program or national strategy. Given the fast-changing AI policy landscape across different geopolitical contexts, a wide-ranging and theoretically grounded analysis was necessary to uncover overarching patterns and systemic risks. However, the reliance on secondary data introduces limitations. The findings depend on the availability and quality of existing research and official reports, which may be biased. As a result, they fail to capture the complexities of real-world experiences, informal policymaking processes, or community responses to AI initiatives.

Future research should use mixed methods to enhance the understanding of AI’s real-world impacts. Researchers can gain valuable insights into the theoretical opportunity costs and governance challenges in various socioeconomic and political settings by conducting interviews with policymakers, technology developers, and affected communities, along with field observations and tracking AI project outcomes over time. This approach will clarify how AI investments affect social welfare, inclusion, and state capacity.

This paper primarily examined national-level AI strategies, focusing on opportunity costs in specific countries. Future studies should incorporate regional frameworks, such as the Digital Transformation Strategy for Africa (2020–2030), to enhance the relevance and connectivity within Africa’s digital agenda. While this study utilizes global economic and governance theories, there is a need for more engagement with African scholars and local social theories. Future research will prioritize African perspectives and localized development frameworks to ensure authenticity and contextual relevance.

Recognizing these limitations is crucial for being transparent about the study’s contributions and for paving the way for future research on the socioeconomic effects of AI investment in the Global South.