• Africa’s AI adoption is accelerating, but its ability to scale depends primarily on foundational investments—especially reliable electricity, digital connectivity, and physical infrastructure—rather than on software innovation alone.
• Governance and skills are emerging as critical gaps, with widespread informal AI use (“shadow AI”) highlighting the need for clearer policies, stronger institutional capacity, and ethical frameworks to manage risks in both corporate and public spheres.
• The strategic opportunity lies in building local capability, including data ecosystems, multilingual models, and research capacity, so Africa moves from AI adoption toward meaningful participation in shaping global AI systems.

Artificial intelligence is moving from concept to deployment across Africa. Global technology companies are expanding compute capacity, governments are investing in digital connectivity, and regional initiatives are laying the groundwork for data-driven economies. From hyperscalers such as Nvidia and Amazon to fibre-optic projects in Nigeria and data infrastructure developments in Kenya, the signals point to growing momentum rather than hesitation.

Yet as AI adoption accelerates, it is becoming clear that scaling the technology sustainably depends less on software breakthroughs than on foundational investments. Reliable electricity remains the most critical enabler. AI systems are energy-intensive, requiring stable power for data centres, cloud services and network infrastructure. In this sense, AI is reinforcing long-standing priorities: power generation, transmission and resilience now carry even higher economic returns.

Beyond electricity, connectivity and physical infrastructure form the next layer. Investments in fibre-optic networks, last-mile access, data centres, logistics facilities and transport networks are essential to ensure AI does not remain confined to a handful of urban hubs. These assets are not new to Africa’s development agenda, but AI significantly increases their strategic value by linking them directly to productivity gains across finance, health, agriculture and public services.

Skills and institutional capacity are equally important. While technical talent is growing, the demand now extends beyond engineers to include data governance specialists, compliance officers, policymakers and managers capable of integrating AI into organisational decision-making. Building this broader skills base will determine whether AI is used merely as a tool for efficiency or as a platform for original innovation.

As adoption expands, governance frameworks are still catching up. Many African organisations are experimenting with AI in an exploratory phase, often relying on informal tools and internal workarounds. Research by CIPESA and Imbila highlights a widening governance gap, with a large share of firms using AI without formal policies. This has contributed to the rise of so-called “shadow AI”, where employees deploy unregulated systems to improve productivity. While this reflects demand and creativity, it also underscores the need for clearer guidelines to manage data security, compliance and misinformation risks.

The governance challenge extends beyond corporate use. Generative AI’s ability to produce content at scale has implications for political communication and public discourse. Early cases documented by civil society groups demonstrate how synthetic messaging can influence online narratives, reinforcing the urgency of transparency, accountability and ethical standards as AI tools become more accessible.

At the same time, market dynamics are broadening access. Lower-cost AI models with lighter computing requirements, including tools developed in China, are gaining traction across the continent. Their appeal lies in affordability and reduced infrastructure demands, making AI usable in resource-constrained environments. However, this trend also raises strategic questions around data localisation, sovereignty and regulatory oversight, highlighting the importance of developing local governance capacity alongside adoption.

Investment trends reflect both progress and limits. African startups have raised more than $200m in AI-related funding over the past two years, signalling growing investor interest. Yet AI still represents a relatively small share of the continent’s overall technology investment, and most deployments focus on operational efficiency rather than the development of new models or datasets. This has drawn attention to the risk of algorithmic exclusion, as many AI systems remain trained primarily on Western data and languages.

Addressing this challenge is less a question of resistance than of capability. Expanding local data ecosystems, supporting multilingual model development and strengthening research capacity can help ensure AI systems better reflect African contexts. With the right investments, AI can move beyond adoption toward genuine participation in shaping global digital technologies.

Africa’s AI trajectory is therefore not defined by a lack of momentum, but by the scale of coordination required. Electricity, infrastructure, skills and governance are not obstacles unique to AI; they are the foundations upon which its impact will be built. As these elements align, AI has the potential to evolve from a productivity enhancer into a driver of inclusive growth and innovation across the continent.

Cynthia Ebot Takang