Is History Repeating Itself in the Heart of Silicon Savannah?
In the bright offices of TechHarambee, a technology startup based in Nairobi, the atmosphere was electric that morning. Aisha Okonkwo, the founder and CEO, had just concluded a video call with Huawei representatives who were proposing a strategic partnership to develop AI solutions adapted to the African market.
« This is an incredible opportunity, » Aisha enthused as she gathered her team. « They bring the technology and funding, we bring our knowledge of the field and local needs. Together, we could deploy AI systems to optimize agricultural yields, improve medical diagnosis in rural areas, and even create educational assistants in our local languages. »
But in a corner of the room, Kwame, the technical director who had lived through two previous waves of technological innovation in Africa, displayed a more reserved demeanor. « I’ve heard these promises before, » he whispered to his colleague. « With the arrival of the internet in the 2000s, then with the mobile revolution in the 2010s. Each time, we were promised radical transformation, a technological leap that would allow us to ‘leapfrog’ development stages. The reality was… more complex. »
This scene, playing out in countless startups across the African continent, perfectly illustrates the paradox facing African entrepreneurs and decision-makers: does artificial intelligence finally represent the technological revolution that will fulfill its promises for Africa? Or is it just a new chapter in a history of imported technologies that struggle to take root and generate sustainable and inclusive development?
As giants like Huawei announce major investments in African AI at events like GITEX Africa 2025, it’s time to examine this question in light of lessons from the past.
Previous Technological Waves in Africa: A Mixed Assessment
To understand the potential of AI in Africa, it’s essential to analyze how previous technological revolutions have transformed (or not) the continent.
The Internet Revolution: Promises and Limitations
In the early 2000s, the arrival of the internet in Africa was supposed to democratize access to knowledge and create new economic opportunities. Twenty years later, the results are nuanced:
Undeniable Successes:
- Emergence of technology hubs in Lagos, Nairobi, Cape Town, and Kigali
- Creation of innovative services adapted to local realities
- Development of mobile payment solutions that were precursors globally
Persistent Limitations:
- Significant digital divide (in 2023, about 40% of the African population had access to the internet)
- Insufficient digital infrastructure in rural areas
- Technological dependence on the West and, increasingly, China
The Mobile Revolution: A True African Success Story
Mobile telephony arguably represents the greatest technological success in Africa. Unlike other regions of the world, Africa went directly from an almost absence of fixed telephony to the massive adoption of mobile.
Notable Innovations:
- M-Pesa in Kenya, which revolutionized financial services and inspired the entire world
- Mobile health applications enabling medical monitoring in remote areas
- Agricultural information services via SMS for small producers
Mobile success in Africa is explained by several key factors:
- Adaptation to local realities (low-cost phones, prepaid recharges)
- Response to concrete, unmet needs (money transfers, access to essential services)
- Viable economic models adapted to local purchasing power
This success has often been cited as an example of « leapfrogging, » where Africa was able to « jump » the costly fixed infrastructure stage to directly adopt more advanced and more suitable technology.
The Data Era: A Partially Seized Opportunity
More recently, the data revolution and big data have offered new perspectives. Initiatives like « Digital Africa » or « Data Science Africa » have emerged to train a generation of African data scientists.
Yet, despite some success stories (such as using data to fight epidemics or optimize agriculture), Africa remains largely a consumer rather than a producer of data-based technologies.
AI in Africa: Current Context and Promising Initiatives
The enthusiasm for AI on the African continent is undeniable. Initiatives such as Google’s AI Research Lab in Ghana, Huawei’s involvement in training local talent, or the creation of alliances like Deep Learning Indaba testify to a bustling ecosystem.
Overview of Current Initiatives
Beyond Huawei’s announcements at GITEX Africa 2025, several initiatives deserve highlighting:
- AI4D Africa (Artificial Intelligence for Development): a pan-African research network that funds AI projects adapted to African challenges
- Zindi Africa: a data science and AI platform that organizes competitions to solve problems specific to the continent
- The Alliance for AI in Africa: a consortium of universities and companies aiming to develop adapted curricula and promote local research
Promising Sectoral Applications
AI is already finding concrete applications in several key sectors:
Agriculture: Startups like Plantix or FarmCrowdy use AI to identify plant diseases, optimize resource use, and connect small farmers to markets.
Health: Initiatives like Ubenwa (which uses AI to diagnose neonatal asphyxia from baby cries) or Zipline (AI-guided drone delivery of medications) show the transformative potential in the medical field.
Education: Platforms like M-Shule or Eneza Education are progressively integrating AI functionalities to personalize learning despite limited resources.
Local Languages: Projects like Masakhane work on natural language processing for African languages, essential for truly inclusive AI.
Pitfalls to Avoid: Lessons from Previous Innovation Waves
While AI offers immense opportunities, the failures of previous technological revolutions in Africa teach us several crucial lessons that entrepreneurs and decision-makers must integrate.
1. The « Imported Technology » Trap
Too often, technologies imported to Africa have been designed for other contexts and simply « transplanted » onto different local realities.
Lesson: African AI must be developed with and for Africans, addressing specific local issues. AI models trained on Western data may perpetuate cultural and social biases unsuited to African contexts.
Positive Example: Mozilla’s « Common Voice » project, which collects voice samples in African languages to create adapted voice recognition systems.
2. The Neglected Infrastructure Trap
Many technological initiatives have failed due to inadequate infrastructure (unstable electricity, limited connectivity, etc.).
Lesson: AI development must be accompanied by investments in fundamental infrastructure, or adapt to existing constraints.
Positive Example: « Edge » or « frugal » AI solutions that can function with limited connectivity or on low computing power devices.
3. The Technological Dependence Trap
Each innovation wave has reinforced Africa’s dependence on foreign technologies.
Lesson: Developing digital sovereignty and local capabilities is crucial for AI to benefit the continent sustainably.
Concerning Sign: The majority of partnerships announced by actors like Huawei involve the use of their proprietary technologies and clouds, without significant transfer of strategic skills.
4. The Technological Elitism Trap
Previous revolutions have often primarily benefited urban elites, deepening inequalities.
Lesson: African AI must be inclusive from its design, accessible in local languages, and adapted to different levels of literacy and digital literacy.
Encouraging Initiative: Projects like « AI for Good » that specifically target rural and marginalized populations.
Conditions for a Truly Transformative AI for Africa
For AI to fulfill its promises in Africa and avoid the disappointments of previous technological waves, several conditions must be met:
Massive Training of Local Talent
Africa must train not only users but also creators of AI technologies. Several initiatives are moving in this direction:
- The « 1 million AI developers » program launched in several countries
- The integration of AI into university curricula, as at the African Institute for Mathematical Sciences
- Bootcamps and intensive training offered by organizations like Andela or ALX
However, the challenge remains immense: according to various estimates, Africa represents only about 1% of AI professionals worldwide.
Development of Properly African Data and Use Cases
AI is only as good as the data it is trained on. Africa must:
- Build data sets representative of its realities
- Document specific use cases that address local challenges
- Create open repositories to pool these resources between countries
Initiatives like « Data Science Africa » or the « Machine Learning Indaba » contribute to this collective effort.
Adapted and Anticipatory Regulatory Frameworks
Unlike previous revolutions where Africa often regulated after the fact, African countries have the opportunity to establish adapted legal and ethical frameworks now:
- Rwanda has already published a national AI policy in 2020
- The African Union is working on a continental framework for AI governance
- Networks such as the African Observatory on Responsible AI are emerging to inform these reflections
These frameworks will need to find a balance between protection and innovation, while reflecting African values and priorities.
Balanced Partnerships and Skills Transfer
Announcements like those of Huawei at GITEX Africa 2025 will only be beneficial if they involve a genuine transfer of knowledge and technologies, rather than simply relocating services at lower cost or opening new markets for foreign technologies.
African entrepreneurs and governments must negotiate partnerships that include:
- In-depth training of local talent
- Sharing of intellectual property
- Localization of data and infrastructure on the continent
- Adaptation to African contexts
Case Study: Mobile Banking vs. Financial AI
An enlightening comparison can be made between the success of mobile banking in Africa and the challenges of AI in the financial sector.
Success Factors for Mobile Banking:
- Local Innovation: M-Pesa was developed in Kenya to address a specific local need
- Ease of Use: Accessible even on basic phones
- Light Infrastructure: Use of the existing GSM network
- Adaptive Regulation: Kenyan authorities allowed experimentation before regulating
Challenges for Financial AI in Africa:
- Technological Complexity: Requires more advanced infrastructure
- Limited Data: Insufficient financial histories for the majority of the population
- Risks of Exclusion: Could widen the gap between banked and unbanked
- Immature Regulatory Frameworks: Few countries have clear rules on financial AI
This comparison shows that AI must draw inspiration from past successes by adapting to local constraints while solving concrete and immediate problems.
Conclusion: African AI at a Crossroads
Back in the offices of TechHarambee in Nairobi, Aisha and Kwame continue their discussion about the partnership proposed by Huawei.
« I understand your concerns, » concedes Aisha. « But I believe that this time, we are better prepared. We have learned from past experiences. We won’t simply accept their technology – we want to co-create solutions, train our teams at all levels of the value chain, and maintain control over our data and use cases. »
Kwame slowly nods. « Maybe that’s where the difference lies. If we approach AI not as an imported miracle solution, but as a tool we can shape according to our needs, then yes, it could truly transform our continent. »
Africa is at a pivotal moment today. AI offers unprecedented opportunities to solve persistent challenges in agriculture, health, education, and many other fields. But these opportunities will only materialize if the lessons from previous technological waves are integrated.
For African entrepreneurs, the message is clear: rather than passively adopting technologies designed elsewhere, they must take ownership of AI, transform it, and adapt it to local contexts. Only under this condition will AI become a true engine of inclusive development, rather than a new episode of disappointed hopes.
As an African proverb says: « If you want to go fast, go alone. If you want to go far, let’s go together. » The future of AI in Africa will depend on our collective ability to go together, combining global technologies and local innovation, global ambitions and responses to the specific needs of the continent.
FAQ about AI in Africa
What are the main obstacles to AI development in Africa? The major obstacles include insufficient infrastructure (stable electricity and connectivity), lack of specialized talent, scarcity of quality local data, and limited access to funding for startups in the field.
Can Africa become a leader in AI rather than just a consumer? Yes, Africa has the potential to become a leader in certain AI niches, particularly those that address challenges specific to the continent. Areas such as AI for sustainable agriculture, low-resource healthcare systems, or African language processing represent opportunities for leadership.
How can AI benefit rural populations and not just urban elites? To benefit rural populations, AI must be developed with a « frugal » approach that works despite infrastructure constraints, be accessible in local languages, and integrate with existing practices. Models such as trained community agents using AI applications on smartphones can serve as intermediaries.
Are partnerships with foreign companies like Huawei beneficial for the African AI ecosystem? These partnerships can be beneficial if they include genuine skills transfer, fair co-creation, and respect for data sovereignty. They become problematic if they create a new form of technological dependence or exploit African data without proportional benefits for the continent.
How can African entrepreneurs position themselves in the global AI ecosystem? African entrepreneurs can stand out by focusing on solving specific local problems, leveraging their in-depth knowledge of African contexts, and developing solutions adapted to the continent’s constraints. Niches where Africa faces unique challenges can become areas of expertise that can be exported globally.
This article analyzes the prospects for AI in Africa in light of previous technological revolutions. What lessons do you think are essential for AI to fulfill its promises on the continent? Please share your opinion in the comments or contact us directly at TheAIExplorer.com.
