Global Perspectives
Arthur Sidney
Public Policy Strategist & Attorney
Arthur Sidney is a public policy strategist, attorney, and former congressional chief of staff focused on AI governance, procurement risk, privacy, and institutional accountability. He advises on technology policy, regulatory strategy, and state and federal affairs. His work examines how governments can retain authority and protect human decision-making as AI systems become embedded in public institutions and critical infrastructure.
Much of the global conversation around artificial intelligence still assumes that African countries are passive recipients of AI systems designed elsewhere. The prevailing narrative is that the United States, China, and the European Union will build the systems, set the rules, and determine the future of governance while the Global South adapts to decisions already made.
That framing is increasingly outdated. Across Africa, governments are beginning to recognize that artificial intelligence governance is not only about passing laws or publishing ethical principles. It is also about leverage. More specifically, it is about who sets the conditions under which AI systems are allowed to operate inside public institutions and across national borders.
Procurement and the Question of Control
In practice, that means procurement. Procurement refers to the process through which governments purchase, authorize, and deploy AI systems and digital infrastructure. In practice, procurement determines who builds the systems, who controls the data, what oversight mechanisms exist, whether systems can be audited, who retains authority to intervene, and how deeply foreign technologies become embedded inside domestic institutions.
AI system design and procurement both matter because they determine whose assumptions become embedded inside public institutions. AI system design includes decisions about what data is collected, which languages are prioritized, how systems categorize people, what outcomes are optimized, and whose values shape the system’s logic. The question is not only who builds the technology, but whose language, social norms, legal traditions, and cultural expectations shape how these systems interpret people and allocate power. These decisions also affect who has access to and control over local data and information, how domestic ideas and datasets are protected, and how governments manage increasingly important cross-border data flows.
The question is not only who builds the technology, but whose language, social norms, legal traditions, and cultural expectations shape how these systems interpret people and allocate power
Without meaningful local participation in AI development, African countries risk becoming sources of raw data and consumers of finished systems while much of the economic value created by AI flows elsewhere.
Procurement is often treated as a technical or administrative process. In reality, it may become one of the most important governance mechanisms in AI. The contracts governments sign with technology vendors can determine who controls data, who has access to infrastructure, whether systems can be audited, whether governments retain the authority to intervene, and how deeply foreign systems become embedded inside domestic institutions.
The central governance question is not simply whether a country adopts AI. It is whether the country retains authority once the system is deployed.
What’s Happening in Africa
The real shift underway is that governments including Rwanda, Kenya, Nigeria, Morocco, and South Africa are no longer approaching AI solely as consumers of foreign technology. They are increasingly attempting to shape the conditions under which these systems operate inside their institutions and societies.
That question matters because AI systems are not neutral. They carry assumptions embedded through design choices, training data, language models, and institutional priorities. Systems developed in Silicon Valley or Beijing may not reflect the social realities, legal traditions, languages, or governance structures of countries in Africa. If governments adopt these systems without meaningful oversight or bargaining power, they risk importing not only technology, but external governance assumptions, data dependencies, and institutional priorities as well.
Language and culture matter here in ways that are often underestimated. AI systems trained primarily in Western languages may fail to capture local meaning, social relationships, historical context, and cultural norms. Governance failures can emerge not only through biased outcomes, but through systems that cannot interpret communities on their own terms. In many African contexts, ideas such as Ubuntu, which emphasizes shared humanity, interdependence, and communal responsibility, offer a fundamentally different social framework than the highly individualistic assumptions often embedded in Western technological systems. As AI becomes integrated into public institutions, these cultural and linguistic questions become governance questions.
If governments adopt these systems without meaningful oversight or bargaining power, they risk importing not only technology, but external governance assumptions, data dependencies, and institutional priorities as well
This challenge is particularly significant across Africa, where linguistic and cultural diversity often exists within the same national borders. Kenya alone, for example, is home to more than fifty languages, while South Africa officially recognizes eleven languages alongside numerous ethnic and cultural communities. The challenge for many governments will not simply be localization, but pluralism: ensuring that AI systems can function across multilingual and multicultural societies without flattening local identities, excluding communities, or privileging one linguistic group over another.
Examples from Rwanda, Kenya, Morocco and South Africa
Some African governments are beginning to approach this challenge strategically.
Rwanda, for example, has pursued AI integration across healthcare, education, agriculture, and public administration while simultaneously investing in domestic technical expertise and local deployment capacity. In healthcare, that includes work around AI-assisted clinical support, digital health systems, and drone-supported medical delivery partnerships aimed at improving rural healthcare access while retaining local institutional oversight. The country has also invested heavily in digital infrastructure, innovation partnerships, and technical training designed to ensure that AI deployment is accompanied by domestic capacity building rather than dependency alone.
Kenya has moved aggressively on digital infrastructure and innovation ecosystems tied to public services, fintech, and digital identity systems, while also participating in broader policy discussions around AI governance and data protection. Kenya’s “Silicon Savannah” ecosystem, a major technology and startup hub centered around Nairobi, and the global success of M-Pesa, one of the world’s most influential mobile payment platforms, helped position the country as a major center for digital innovation, fintech development, and AI-enabled services connected to finance and mobile infrastructure.
Governments across the continent are increasingly aware that control over local data and digital infrastructure is tied directly to long-term political and economic leverage
Morocco has positioned itself as a regional technology and digital governance hub connecting Africa, Europe, and the Middle East, including investments in digital infrastructure, smart governance initiatives, and multilingual technological development. The country has also invested heavily in smart city initiatives and digital modernization projects tied to transportation, energy, and public administration.
Nigeria, meanwhile, has increasingly emerged as a center for AI entrepreneurship, digital policy debate, and startup activity connected to finance, language technologies, education, agriculture, and public services. Nigerian startups and researchers are increasingly developing tools focused on African languages, financial inclusion, digital payments, and educational access for local markets often underserved by larger global platforms.
South Africa, meanwhile, has attempted to anchor AI governance discussions within the country’s broader constitutional and human rights traditions, including ongoing debates around inclusion, inequality, multilingualism, and public accountability in digital systems. Even the country’s withdrawn draft AI framework reflected an effort to align technological governance with domestic social and constitutional realities rather than simply importing external governance assumptions wholesale.
Countries including Rwanda, Kenya, Nigeria, Morocco, and South Africa are increasingly attempting to shape AI systems in ways that reflect African priorities, cultures, governance traditions, and linguistic realities rather than simply inheriting external models wholesale. In practice, that means investing in local-language datasets, multilingual AI development, domestic technical talent, regionally relevant training data, and governance frameworks attentive to local constitutional traditions and social norms.
It also means seeking greater control over how data is collected, stored, transferred, and used. Through national initiatives and wider coordination within the African Union, governments across the continent are increasingly recognizing that control over local data and digital infrastructure is closely linked to long-term political and economic influence.
Together, these efforts point to a broader shift toward strengthening local capacity and reducing dependence across parts of the continent.
From Periphery to the Center
Overlooked in many global discussions is the growing ecosystem of African startups building AI tools for local markets and social realities. Across the continent, founders are developing systems focused on agriculture, fintech, healthcare, education, translation, logistics, and public services, often designed around local languages and needs neglected by larger global models.
Some governments are beginning to treat AI not merely as a technology issue, but as a question of sovereignty, institutional authority, and long-term leverage
At the same time, governments, universities, incubators, and regional innovation hubs are investing more in local technical talent, digital infrastructure, startup accelerators, and public-private partnerships. The goal is to ensure that African countries are not only consumers of AI, but also active contributors to its development. Companies such as South Africa-based Lelapa AI, for example, are developing language models and other AI systems that reflect the continent’s linguistic diversity rather than treating African languages as an afterthought.
In some respects, parts of Africa are ahead of the United States. The United States remains the global leader in AI development and market power, but it still lacks a unified federal AI framework. In the absence of comprehensive legislation, governance is increasingly occurring through executive action, procurement decisions, agency guidance, and fragmented state-level initiatives. The result is a governance structure that remains highly decentralized and politically unstable.
By contrast, governments including Rwanda, Kenya, Morocco, South Africa, and Nigeria are experimenting more directly with institutional design, procurement-centered governance, digital sovereignty, and state capacity building around AI deployment. These efforts are uneven and still developing, but they reflect an important shift: some governments are beginning to treat AI not merely as a technology issue, but as a question of sovereignty, institutional authority, and long-term leverage.
Who Retains the Power to Shape AI Systems
The geopolitical implications are significant.
Artificial intelligence will influence healthcare systems, labor markets, education, finance, border management, and public administration. Countries that cannot negotiate the terms of deployment may eventually find themselves dependent on external systems they cannot fully audit, explain, or control.
That dependency risk matters because once systems become embedded, institutions reorganize around them. Data accumulates around them. Workflows adapt to them. Public services become dependent on them. At that point, the governance question is no longer simply whether the technology works. It is whether governments still retain meaningful authority over systems that have become operationally indispensable.
At that point, the governance question is no longer simply whether the technology works. It is whether governments still retain meaningful authority over systems that have become operationally indispensable
That is why procurement matters. A government that controls procurement terms can demand localization requirements, transparency obligations, audit rights, data protections, human oversight mechanisms, and meaningful local participation in deployment and maintenance. A government that lacks bargaining power may instead inherit systems optimized around someone else’s political, economic, and cultural priorities.
Regional coordination may become increasingly important here as well. Through organizations such as the African Union, African governments may have greater leverage negotiating collectively with multinational technology firms than they would individually. Market access, critical minerals, demographic growth, and digital infrastructure needs all create bargaining power that did not exist at the same scale a decade ago.
The broader lesson is that AI governance is not fundamentally about slogans, ethics statements, or aspirational principles. Governance becomes real only when institutions retain the authority and practical ability to enforce decisions after deployment. The future of AI governance may therefore depend less on who builds the most advanced systems and more on who retains the power to shape, supervise, and if necessary stop them once they enter society.