Introduction

While the world races toward an AI-dominated future, Africa stands at a technological crossroads that will define the continent's prosperity for generations. The promise is extraordinary: AI could revolutionize healthcare in underserved communities, transform agricultural yields in the face of climate change, and create educational opportunities for millions. But there's a dangerous illusion taking hold—that Africa can simply take Western AI models, "finetune" them with a sprinkle of local data, and watch the magic happen.

This technological hand-me-down approach isn't just insufficient—it's a path to digital colonialism masquerading as progress.

The reality is stark: finetuning—the process of adapting pre-trained AI systems with specific datasets—fundamentally fails to address the complex web of challenges unique to African contexts. While tech evangelists promote quick fixes, the continent faces a perfect storm of obstacles that no amount of algorithmic tweaking can overcome.

Consider this: when you query a large language model about African topics, responses are often thin, inaccurate, or completely fabricated. This isn't a bug—it's a feature of systems built on datasets where Africa is virtually invisible. Only 2% of global healthcare data comes from Africa, despite the continent bearing a disproportionate disease burden. How can we expect finetuned models to diagnose malaria when they've barely seen it?

The barriers extend far beyond data scarcity. Africa's average AI readiness score sits at a mere 26.91 out of 100. Base models contain deeply embedded biases that finetuning merely masks rather than eliminates. Research capacity remains constrained, with African voices dramatically underrepresented in global AI development. Meanwhile, outdated regulatory frameworks struggle to govern technologies that evolve by the day.

Africa doesn't need slightly adjusted versions of Silicon Valley's technological visions. It needs a fundamental reimagining of AI development that centers African data, expertise, and priorities from the ground up. This isn't just about fairness—it's about effectiveness. AI solutions that fail to account for the continent's unique contexts will inevitably fail, regardless of how many layers of finetuning are applied.

The path forward requires building strong foundations rather than applying quick fixes. This means investing in comprehensive data collection efforts, developing local infrastructure, building homegrown expertise, and creating governance frameworks that reflect African realities. Only then can AI truly serve the continent's needs rather than simply extending foreign technologies with a veneer of local relevance.

The stakes couldn't be higher. As AI reshapes global power structures, Africa's technological sovereignty hangs in the balance. The choices made today will determine whether the continent becomes a leading creator of contextually appropriate AI or merely a consumer of systems designed for other worlds.

The Data Deficit

The phrase "garbage in, garbage out" haunts every AI project. But in Africa's case, it's more like "nothing in, nothing out." The continent suffers not just from bad data, but from a devastating absence in the datasets that power the world's AI systems.

When users ask large language models about African topics, the response quality plummets. Not because these systems are deliberately biased, but because they've barely encountered Africa during their training. It's like asking someone to speak a language they've only heard spoken for five minutes. The results aren't just disappointing—they're dangerous.

The Healthcare Data Crisis

The numbers tell a shocking story: while Africa shoulders 25% of the global disease burden, it contributes only 2% of the world's healthcare data. This isn't a minor statistical anomaly—it's a life-or-death representation crisis.

Imagine trying to train an AI system to detect tropical diseases when the vast majority of its training images come from European or North American patients. The model might excel at identifying melanoma on pale skin but fail catastrophically when faced with the same condition on darker skin tones. Finetuning such a system might produce modest improvements, but it's building on a fundamentally flawed foundation.

The Language Barrier: 2,000+ Voices Silenced

Africa's linguistic diversity represents one of humanity's greatest cultural treasures—and one of AI's most profound blind spots. With over 2,000 languages spoken across the continent, most with minimal digital footprints, the majority of African linguistic expressions remain completely invisible to AI systems.

This exclusion creates a vicious cycle: AI performs poorly for African languages, so users default to colonial languages like English or French for digital interactions, which further reduces the digital presence of indigenous languages. Each generation loses more fluency, and millennia of knowledge encoded in these languages—from traditional medicine to environmental management—remains inaccessible to AI systems.

Voice-to-text technologies have been proposed as potential solutions, but these technologies themselves struggle with African accents and languages due to—you guessed it—insufficient training data. The recursive problem becomes painfully clear.

The Digitization Dilemma

Even when valuable African data exists in analog form—in handwritten medical records, oral histories, or traditional knowledge systems—the path to digitization is blocked by formidable obstacles.

Financial constraints make large-scale digitization projects prohibitively expensive in regions already struggling with poverty. Digital literacy challenges limit community participation in data collection efforts. Questions of data ownership and sovereignty remain unresolved, raising legitimate concerns about who benefits when African data enters global AI systems.

Without addressing these fundamental issues, finetuning becomes an exercise in technological wishful thinking—attempting to correct deeply embedded patterns of exclusion through superficial adjustments to models that lack the foundational understanding of African realities.

The data deficit is not just a technical challenge but a manifestation of global power imbalances that have systematically devalued and excluded African knowledge. Solving it requires more than incremental technical solutions—it demands a fundamental shift in how we value, collect, and incorporate diverse forms of knowledge into our AI systems.

Technical Infrastructure Limitations

Discussing AI finetuning in many African contexts is like planning a Formula 1 race in a region without paved roads. The fundamental infrastructure required doesn't just need improvement—in many cases, it barely exists.

The Reality Check: 26.91 Out of 100

The cold, hard numbers expose a stark reality: according to the AI Startups Mapping in Africa report by AfriLabs, the continent's average AI readiness index stands at a mere 26.91 out of 100. Even the continent's most technologically advanced nations—Egypt, Kenya, South Africa, and Tunisia—score between 40 and 50, revealing critical gaps in the foundational capabilities needed for meaningful AI development.

This isn't just a slight disadvantage—it's playing an entirely different game. While global tech giants deploy thousands of specialized GPUs and TPUs to train increasingly complex models, many African institutions struggle to maintain basic computing infrastructure amid power outages and connectivity challenges.

The Hardware Hurdle

Let's be crystal clear: modern AI development requires specialized hardware. Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs)—the workhorses of machine learning—remain prohibitively expensive and difficult to access throughout most of Africa. A single high-end GPU can cost more than a year's salary for many professionals in the region.

Without this computational backbone, African developers face a cruel paradox: they need to adapt global AI models to local contexts, but lack the very tools required to perform that adaptation effectively. It's a technological Catch-22 that keeps the continent dependent on external solutions regardless of how poorly those solutions fit local needs.

The Power Problem

Training sophisticated AI models isn't just computationally intensive—it's energy-hungry. A single large language model training run can consume more electricity than hundreds of households use in a year. In regions where power outages occur daily and diesel generators fill the gaps, the idea of running compute-intensive AI operations becomes practically impossible.

Energy instability doesn't just limit training capabilities—it undermines every aspect of the AI ecosystem. Data centers require constant power. Cloud services demand reliable connectivity. Even the most brilliantly designed AI systems falter when the electricity cuts out mid-computation.

The Infrastructure Ecosystem

Technical infrastructure isn't just about individual components—it's about interconnected systems working in harmony: robust data storage solutions, cloud computing platforms, reliable communication networks, and consistent power all functioning together seamlessly.

Throughout much of Africa, these components remain deeply fragmented. While smartphone penetration has increased dramatically, providing front-end access points, the back-end infrastructure needed to develop and deploy sophisticated AI applications lags far behind. The result is a widening technological gap that threatens to further marginalize the continent in the global AI revolution.

The Financial Reality

Building world-class AI infrastructure requires massive capital investment—resources that many African institutions simply don't have. Without significant external funding or innovative financing models, the infrastructure gap will continue to widen as global competitors race ahead with increasingly advanced technologies.

This harsh financial reality creates a sobering truth: even with perfect datasets and world-class expertise, many African AI practitioners would still face insurmountable physical limitations in executing sophisticated finetuning processes. The infrastructure constraints create a practical ceiling on local AI adaptation efforts that no amount of ingenuity can overcome without substantial investment.

The technical infrastructure challenge isn't just about catching up—it's about leapfrogging outdated paradigms to build systems specifically designed for African contexts and constraints. Without addressing these fundamental infrastructure gaps, discussions of AI finetuning remain largely academic exercises disconnected from on-the-ground realities.

Bias and Cultural Misalignment: When AI Speaks With a Foreign Accent

Imagine building your future on technology that's fundamentally biased against you. This isn't hypothetical—it's the reality facing African users of AI systems finetuned from Western models. The problem goes far deeper than missing data points; it's embedded in the very architecture of these systems.

Baked-In Bias: More Than Skin Deep

Recent studies have uncovered alarming evidence of how deeply prejudice penetrates large language models. These systems don't just reflect biases—they amplify them. Research reveals that AI systems exhibit strong raciolinguistic stereotypes against speakers of African American English, suggesting that users would be more suitable for less prestigious jobs, more likely to be convicted of crimes, and even more deserving of death sentences.

This isn't a minor calibration issue—it's a fundamental flaw. When the foundation itself is built on datasets reflecting centuries of discrimination, no amount of finetuning can fully excavate these biases. At best, adaptation creates a thin veneer of fairness while the underlying mechanics continue to perpetuate harmful patterns.

The Masking Effect: Hidden Biases Becoming More Dangerous

Even more troubling is the emerging pattern where larger language models and those trained with human feedback display stronger covert prejudice while showing weaker overt bias. In plain language: as models become more "acceptable," they don't eliminate discrimination—they simply hide it better.

This pattern mirrors societal dynamics where explicit racism becomes taboo while structural inequalities persist through more subtle mechanisms. For African contexts, this creates the disturbing possibility that finetuning might create an illusion of appropriateness while actually perpetuating or even amplifying underlying biases that disadvantage African users.

Cultural Imperialism by Algorithm

The misalignment between AI and African contexts extends far beyond language to encompass fundamental worldviews and value systems. Western-developed AI models embed assumptions about individualism, linear progress, and market-based solutions that often clash with diverse African perspectives emphasizing community, cyclical thinking, and collaborative approaches.

These cultural disconnects aren't just academic concerns—they shape how AI systems interpret situations, prioritize information, and recommend actions. When AI is deployed to make decisions about healthcare resource allocation, financial lending, or educational opportunities, these embedded Western perspectives can lead to fundamentally misguided recommendations despite superficial localization through finetuning.

Real-World Consequences

The implications extend far beyond theoretical concerns. Research documents how human landlords engage in housing discrimination based solely on auditory profiles of speakers, with voices perceived as Black or Chicano less likely to secure housing appointments in predominantly white areas.

AI systems trained on data reflecting such discriminatory patterns risk automating and scaling these biases, potentially creating new forms of technological exclusion that disproportionately affect African populations. As these systems increasingly influence critical decisions in lending, healthcare, education, and employment, the potential for harm grows exponentially.

The unsettling reality is that in many cases, finetuning doesn't solve these problems—it obscures them beneath a veneer of adaptation while leaving the fundamental biases intact. Creating truly equitable AI systems for African contexts requires more than surface adjustments; it demands rebuilding from the ground up with African data, perspectives, and priorities at the center rather than the periphery.

Anything less risks technological colonialism dressed in the language of inclusion—AI systems that claim to serve African needs while subtly reinforcing external worldviews and priorities.

The Expertise Gap: Why Technical Skills Alone Aren't Enough

While glossy tech conferences showcase African innovation, a sobering reality lurks behind the headlines: despite decades of training and capacity building, Africa's research output remains dangerously low. This isn't just about academic prestige—it's about the fundamental expertise needed to adapt AI systems to local contexts.

The Publication Paradox

The numbers tell a troubling story: Africa's share of global scientific publications grew from 1.5% in 2005 to 3.2% in 2016. While this represents progress, it remains wildly disproportionate to the continent's population and needs. This publication gap isn't merely a matter of academic metrics; it reflects a deeper challenge in building and retaining the technical talent necessary for sophisticated AI development and adaptation.

When African researchers struggle to participate in global knowledge creation, the continent's realities remain undocumented in the scientific literature that forms the foundation of technological development. The expertise gap creates a knowledge vacuum that finetuning alone cannot fill, no matter how sophisticated the technical approach.

The Multidimensional Expertise Challenge

Effective AI finetuning isn't just a matter of knowing machine learning algorithms. It requires a complex blend of skills spanning multiple domains:

• Deep technical knowledge of machine learning systems
• Data science expertise for processing and analyzing local data
• Domain specialization in the application area (healthcare, agriculture, etc.)
• Contextual understanding of local needs, constraints, and cultural factors
• Ethical frameworks suited to local contexts and priorities

This multifaceted expertise remains scarce across much of Africa, forcing institutions into difficult choices between importing external expertise that lacks contextual understanding or proceeding with limited local resources that may be insufficient for sophisticated AI adaptation.

The Vicious Cycle of Capability Development

The expertise challenge is perpetuated by structural issues that create a damaging cycle: limited local expertise leads to reduced capacity for locally-driven innovation, which limits the development of contextually appropriate solutions, which in turn fails to demonstrate the value of further investment in local capacity building.

Brain drain compounds the problem, as talented professionals seek opportunities abroad after finding limited pathways at home. Research funding remains scarce, creating few opportunities for sustained professional development in cutting-edge AI techniques. Educational systems struggle to keep pace with rapidly evolving technologies, creating persistent gaps between training and real-world requirements.

Finetuning Without Context: A Recipe for Failure

The expertise gap has particular significance for finetuning approaches because effective adaptation requires deep understanding of both the AI systems being modified and the contexts to which they are being adapted. As MISA regional chairperson Golden Maunganidze emphasized, sustainable adoption of AI in Southern Africa requires capacity building alongside the development of local datasets.

Without sufficient local expertise guiding adaptation processes, finetuning efforts risk missing critical contextual factors, misinterpreting local needs, or implementing technical approaches that fail to address fundamental misalignments between existing models and African realities.

The much-anticipated hope that locally generated evidence will inform the development of local solutions remains largely unrealized without substantial investment in building both research capacity and technical expertise as foundations for contextually appropriate AI development.

Building this expertise can't be accomplished through short-term training programs or occasional workshops. It requires sustained investment in educational ecosystems, research institutions, professional development pathways, and innovation hubs where technical capabilities can develop in conversation with contextual knowledge. Without these foundations, finetuning remains a technical solution to a challenge that is fundamentally about human capacity and knowledge systems.

Regulatory and Governance Challenges: Building the Plane While Flying It

As AI technologies race forward at breakneck speed, Africa's regulatory frameworks are struggling to keep pace—not just a few steps behind, but often entire paradigms behind. This regulatory lag isn't just an administrative inconvenience; it's a fundamental barrier to responsible AI development and adaptation across the continent.

The Regulatory Vacuum

Research conducted by the Media Institute of Southern Africa (MISA) and supported by Mozilla paints a concerning picture: regulatory frameworks for AI across much of Africa remain severely underdeveloped. During a launch event for this research, Zimbabwe's Minister of Information and Communications, Tatenda Mavetera, acknowledged the painful truth—existing regulatory frameworks are woefully outdated, "causing fragmentation and duplication of processes within institutions."

This regulatory vacuum creates a treacherous landscape for organizations attempting to finetune and deploy AI systems. Without clear guidelines, boundaries between acceptable and unacceptable practices remain blurry, creating uncertainty that stifles innovation while simultaneously leaving vulnerable populations unprotected from potential harms.

Beyond Outdated Policies: Fundamental Governance Questions

The governance challenges extend far beyond simply updating old policies. They encompass fundamental questions that remain largely unaddressed across much of the continent:

• Who owns the data used to train and finetune AI systems?
• What standards of transparency should AI systems meet?
• Who bears liability when AI systems cause harm?
• What recourse do citizens have when affected by algorithmic decisions?
• How should AI systems be tested before deployment in critical areas?

Without clear frameworks addressing these issues, organizations face significant risks when deploying finetuned AI systems, potentially leading to either overly cautious approaches that limit innovation or insufficient safeguards that harm vulnerable populations.

The Temporal Mismatch: Governance at AI Speed

The governance challenge is compounded by a fundamental temporal mismatch: AI technologies evolve at exponential speeds while regulatory processes move at linear—often glacial—paces. This creates persistent gaps between technological capabilities and governance frameworks, leaving critical ethical questions unaddressed and creating opportunities for misuse.

This mismatch particularly impacts vulnerable populations, who often bear the brunt of technological experimentation without adequate protections. As finetuned AI systems deploy across sectors from healthcare to financial services, the absence of appropriate governance creates significant risks that disproportionately affect those with the least power to address resulting harms.

The Transnational Complexity

Adding further complexity, AI governance inherently crosses national boundaries. African countries must navigate intricate questions about data sovereignty, cross-border data transfers, and relationships with multinational technology companies that often develop the base models used in finetuning approaches.

These international dimensions require coordination across national boundaries and engagement with global governance initiatives. Yet without strong positions in these international conversations, African countries risk having their specific concerns and priorities marginalized in global AI governance frameworks, potentially leading to regulatory systems that fail to address their unique contexts and needs.

The Risk of Misuse

The MISA research specifically identifies potential misuse of AI as one of the three key challenges facing southern Africa in AI adoption. The Zimbabwe August 2023 election case study mentioned in the research suggests that concerns about AI misuse in politically sensitive contexts are already emerging, underscoring the urgency of developing appropriate governance mechanisms.

Without robust regulatory frameworks, finetuning efforts may inadvertently enable problematic applications or face significant resistance from communities concerned about potential misuse, limiting their overall effectiveness and acceptability regardless of technical merit.

The governance challenge requires not just policy updates but comprehensive regulatory reimagining that reflects the unique conditions and priorities of African contexts. This isn't just about restriction—it's about creating enabling environments where beneficial AI applications can flourish while harmful ones are effectively constrained. Developing these frameworks with broad stakeholder involvement is essential to ensure they reflect local contexts and priorities while providing the stability needed for sustainable AI development.

Building Foundations for Contextually Appropriate AI: Beyond Finetuning

The challenges facing AI adaptation in Africa may seem overwhelming, but they also create a unique opportunity: the chance to build AI systems that truly serve African needs rather than simply importing solutions designed for other contexts. This requires shifting from quick technological fixes to foundational investments that can support truly contextually appropriate AI development.

Creating Representative African Datasets

The first critical foundation is addressing the severe data deficit that undermines AI performance for African contexts. This demands collaborative efforts to document and digitize diverse African languages, cultural knowledge, and contextual information currently missing from global datasets.

Innovative approaches like those proposed by Bayo Adekanmbi of Data Science Nigeria offer promising paths forward. Voice-to-text technologies could help document traditionally oral knowledge systems, creating digital records that preserve both information and cultural context. Community-led data collection initiatives can ensure that datasets reflect local priorities and perspectives rather than external assumptions.

This data foundation must extend beyond simply collecting information to establishing appropriate frameworks for data ownership, sovereignty, and ethical use. African institutions must be empowered to control how their data is used rather than simply contributing to datasets controlled by external entities.

Infrastructure Development: Building for African Realities

Overcoming the infrastructure gap requires targeted investments in digital foundations specifically designed for African contexts. The AfriLabs report's sobering assessment of Africa's AI readiness (average index of 26.91) highlights the urgent need for infrastructure development that addresses unique local challenges.

Rather than simply replicating infrastructure models from other regions, Africa has the opportunity to develop innovative approaches that reflect local realities:

• Regional AI research centers that provide shared computing resources
• Distributed computing models that leverage collective resources across institutions
• Energy-efficient AI approaches designed for power-constrained environments
• Cloud infrastructure specifically optimized for African connectivity challenges

These infrastructure investments must be viewed not as luxuries but as essential foundations for enabling African countries to participate meaningfully in the global AI ecosystem and adapt technologies to address local challenges effectively.

Capacity Building: Developing Multidisciplinary Expertise

Addressing the expertise gap requires comprehensive approaches to capacity building that go beyond technical training to develop the multidisciplinary knowledge needed for contextually appropriate AI. As MISA regional chairperson Golden Maunganidze has emphasized, this requires "the involvement of African innovators, policymakers, and academics in AI conversations to ensure solutions align with African needs and priorities."

This capacity building must span multiple domains:

• Technical training in AI development and adaptation
• Domain expertise in critical application areas like healthcare and agriculture
• Contextual knowledge of local needs and constraints
• Ethical frameworks rooted in African value systems
• Policy expertise to shape appropriate governance structures

Educational initiatives must be coupled with professional development pathways and research opportunities that allow African experts to apply their skills locally rather than seeking opportunities abroad. By investing in local universities, research institutions, and innovation hubs, countries can create ecosystems where technical capabilities develop in conversation with contextual knowledge.

Forward-Looking Regulatory Development

Creating appropriate regulatory frameworks isn't just about constraining AI development—it's about enabling responsible innovation that serves local needs. As Zimbabwe's Minister of Information and Communications noted, updated regulatory frameworks are essential to prevent fragmentation and duplication of processes.

These frameworks should include:

• Clear standards for algorithmic transparency and accountability
• Frameworks for data protection that respect local privacy values
• Mechanisms for assessing and addressing potential harms
• Structures for meaningful community engagement in AI governance
• Approaches to cross-border data flows that respect sovereignty

By developing these frameworks with broad stakeholder involvement, African countries can create governance structures that reflect their specific contexts and priorities while providing the stability needed for sustainable AI development and adaptation.

Collaborative Models Centered on African Priorities

Perhaps most importantly, moving beyond finetuning requires new models of collaboration that center African priorities and perspectives. This means shifting from adaptation of existing models to co-creation of new approaches that reflect African realities from the outset.

These collaborative models must recognize the value of diverse knowledge systems and create spaces where technical expertise can engage meaningfully with contextual understanding. By bringing together technologists, domain experts, community representatives, and policymakers, these collaborations can develop AI approaches that truly serve local needs rather than simply extending technologies developed for other contexts.

The path forward isn't about rejecting global AI advances but about ensuring that African voices, data, and priorities shape how these technologies develop and deploy across the continent. By building these essential foundations, stakeholders can work toward AI futures that reflect Africa's diverse realities rather than perpetuating technological dependencies and exclusions.

Conclusion: From Adaptation to Innovation

The challenges facing AI development and deployment across Africa create a perfect storm that no amount of finetuning can adequately address. The data deficit, with only 2% of global healthcare data coming from Africa despite the continent's significant health challenges, creates a fundamental representational gap. The troubling evidence of deep-seated bias against African linguistic patterns in large language models raises profound questions about whether models trained primarily on Western data can ever adequately serve African populations. The substantial infrastructure gap, reflected in Africa's average AI readiness index of 26.91, demonstrates the scale of investment needed to build basic capabilities. The persistent underrepresentation of African research in global scientific publications reveals broader structural issues in knowledge production that undermine locally-driven innovation.

Together, these interconnected challenges make clear that the path forward lies not in incremental adaptation of external technologies but in fundamental approaches to building AI systems that reflect African realities from the ground up.

Beyond Digital Colonialism

The temptation to view AI as a purely technical challenge leads to solutions that ignore the social, cultural, and political dimensions of technology. Finetuning existing models without addressing fundamental representational imbalances risks creating a new form of digital colonialism—one where African data and contexts are incorporated just enough to create the appearance of inclusion while deeper patterns of exclusion persist.

Moving beyond this pattern requires recognizing AI development as not just a technical process but a social one that reflects and reinforces power relationships. Building truly contextually appropriate AI means confronting these power dynamics directly and creating development approaches that center African agency and priorities rather than treating them as afterthoughts.

The Opportunity in Challenge

Despite these substantial challenges, there remains tremendous potential for AI technologies to address pressing needs across African contexts—if approached with appropriate humility, collaboration, and commitment to building necessary foundations. The very lack of entrenched AI infrastructure creates opportunities to leapfrog outdated approaches and develop systems specifically designed for African contexts and priorities.

Initiatives like MsingiAI where I am working represents the kind of groundbreaking work needed to create truly African AI solutions. By building from the ground up with African languages, contexts, and priorities at the center, MsingiAI exemplifies the shift from adaptation to innovation that the continent needs. Such pioneering efforts demonstrate that with the right approach, African AI can move beyond merely catching up to actually leading in creating contextually appropriate technologies.

The path forward requires substantial investment, patience, and genuine partnership between African institutions and global technology developers. It demands moving beyond quick fixes toward sustained commitment to building the data resources, infrastructure, expertise, and governance frameworks needed to support contextually appropriate AI. Most importantly, it requires centering African voices, priorities, and knowledge systems in the development process rather than treating them as mere inputs to externally-driven agendas.

A Vision for African AI

Imagine AI systems that recognize all 2,000+ African languages, that understand local cultural contexts and values, that address the specific challenges facing African communities—from healthcare to agriculture to education. Imagine technologies developed by African experts, trained on representative African data, governed by frameworks that reflect African priorities, and deployed on infrastructure designed for African realities.

This vision cannot be achieved through finetuning alone. It requires fundamental reimagining of how AI is developed, deployed, and governed. It demands moving from adaptation to innovation—from importing external solutions to creating uniquely African approaches to artificial intelligence.

The stakes could not be higher. As AI reshapes global power structures and economic relationships, the choices made today will determine whether African countries become full participants in the AI revolution or remain on its margins. By addressing the fundamental challenges outlined in this article and committing to building strong foundations for contextually appropriate AI, stakeholders can work toward technological futures that empower rather than marginalize African communities.

The future of AI in Africa lies not in finetuning but in fundamentally reimagining how these technologies are developed and deployed. By centering African data, expertise, contexts, and priorities, the continent can move beyond adaptation toward true technological sovereignty—creating AI systems like MsingiAI that genuinely serve its diverse peoples and their aspirations.