When AI met Africa’s roadblocks—what went wrong

Remember when everyone thought AI would change everything overnight? In many African countries, that excitement quickly met reality. Even tech-forward nations like South Africa, Kenya, Nigeria, and Ghana faced big challenges just getting AI off the ground.

As we saw in the first article, Africa’s AI story is marked by contrasts; some places buzz with innovation, and others still struggle with basics like electricity and internet access. Before 2018, even the stronger economies hit roadblocks like shaky infrastructure, talent leaving home, and slow government action.

This article looks back at those early struggles to understand how Africa’s AI journey really began and why it’s been a slow climb, not a quick leap.

High expectations, little execution

Back then, the AI buzz was loud, especially in places like Nairobi, Lagos, and Cape Town. Everyone was talking about how it could revolutionize everything from farming to healthcare. But turning that hype into action? Not so easy.

Most projects were small, short-lived, and heavily reliant on donor funding. Without strong local support or long-term plans, they often fizzled out once the money stopped flowing. In countries like Rwanda and Senegal, early AI efforts in health and agriculture couldn’t survive because the local tech ecosystems weren’t ready to carry them forward. It was like planting seeds in dry soil—full of potential but missing the basics to grow.

Infrastructure and technical limitations

South Africa, Nigeria, and Kenya entered the AI race with big goals: smarter cities, better farming, and improved public services. South Africa planned to use AI in mining and digital government, while Kenya and Nigeria focused on agriculture and FinTech. Strategies were launched and pilot projects started, but cracks formed quickly.

Internet, electricity, and cloud access were unreliable, especially outside big cities. In South Africa, rural areas stayed disconnected. Nigeria and Kenya faced frequent power cuts, weak internet, and few local data centers. The basics weren’t ready. High-performance computing was expensive; GPUs and cloud services cost up to 31 times more than in wealthy countries, forcing many teams to rely on foreign platforms. That caused delays, high costs, and worries about data control.

By 2023, only 36% of Africans were online—just half the global rate. That number alone made national AI rollouts a distant goal. Most projects stayed stuck in urban labs or academic settings. The ambition was real, but the foundation was missing. Without stable infrastructure, grand visions of AI rarely reached the people they were built for.

Government: Absent or unprepared

Until 2019, most African governments lacked clear AI strategies—no policies, funding, or coordination. South Africa mentioned digital innovation but had no AI plan. Nigeria focused on broadband and e-governance, overlooking AI’s economic role.

Ministries worked separately without a shared vision for AI investment or ethics. This caused collaboration to stall, reforms to slow, and left researchers and start-ups isolated and unsupported.

Even countries like Rwanda and Mauritius, early to draft AI strategies, were slowed by delays in decision-making and lack of follow-through.

In 2024, South Africa introduced a National AI Policy Framework aimed at transforming the landscape. The framework prioritized skill development; infrastructure expansion; responsible AI adoption; and fostering collaboration between government, industry, and academia. It also emphasized transparency and inclusivity.

Nevertheless, implementation challenges persisted, with infrastructure deficiencies in rural areas and a shortage of qualified professionals potentially hindering progress.

Talent drain and training gaps

AI wasn’t a priority in many African universities at first. Most programs stuck to basic tech training, with few experts to teach or lead AI research. As a result, talented students often left for better opportunities abroad, deepening the brain drain.

Governments also gave little support to build local AI talent. Without strong funding or fellowships, it was hard to train or keep skilled professionals. Even with recent efforts in places like South Africa, the gap between AI demand and local talent remains wide.

According to a 2025 SAP report, 53% of organizations in South Africa, 50% in Nigeria, and 43% in Kenya report significant shortages in AI skills. These gaps lead to project delays, failed innovations, and decreased competitiveness.

The World Economic Forum (2024) highlights that Africa accounts for only about 2.5% of global AI researchers, underscoring how the persistent talent shortage and brain drain continue to limit the development of a local AI ecosystem and hinder the success of national AI strategies.

Data scarcity and access problems

Africa holds less than 1% of the world’s data center capacity, with only about 152 data centers compared to thousands found in other regions. This shortage severely limits the continent’s ability to process and store the massive amounts of data that AI requires, slowing down AI development and its practical use. South Africa dominates Africa’s data center market with a 50% share, yet it still faces infrastructure gaps, especially in rural areas.

Another major challenge was accessing public datasets. Much of the data was outdated, not digitized, or stuck in bureaucratic red tape. Governments were often hesitant to share it, even when it could benefit key sectors like health and agriculture. The lack of local language datasets also made teams rely on foreign data, weakening the accuracy and relevance of AI models.

Weak start-up support

Even in tech-forward countries like South Africa and Nigeria, many early AI start-ups struggled. Investors avoided long-term projects without quick returns, while governments lacked funding schemes and policies to support AI. Most incubators couldn’t guide deep-tech ventures, and public-private support was limited. As a result, many start-ups lacked access to data, infrastructure, and funding, forcing them to pivot or shut down. This weak support system slowed the growth of AI innovation across the region.

A bold start, but at what cost?

Africa didn’t step quietly into the AI era—it entered with bold visions but ran headfirst into tough realities. Poor internet, power cuts, limited data, and high computing costs slowed progress, even in stronger economies like South Africa and Rwanda. Universities lacked AI focus, governments moved slowly, and investors avoided long-term risks. Talent left, and start-ups struggled to survive. These early challenges weren’t the end; they laid the groundwork.

From 2018 to 2023, things began to change. Countries started crafting real AI strategies and testing its potential across sectors like agriculture, finance, health, and education. In the next article, we’ll explore how AI is making a difference on the ground and the improvements driving Africa’s AI future forward. Stay tuned.