By: Hassan Mudane
Artificial Intelligence is no longer a distant technological frontier. It is becoming the backbone of productivity, governance, finance, logistics, agriculture, and national competitiveness. Around the world, countries are reorganizing policy, education, and investment around AI because they understand a simple reality: those who adapt AI systems will shape economic power for decades to come.
In East Africa, this reality is already taking shape. Kenya and Ethiopia have begun embedding AI into national strategies, institutions, and innovation ecosystems. Somalia, by contrast, remains at an early and fragmented stage of AI development despite having one of the region’s most dynamic private sectors.
Comparing these three countries is not only an academic exercise. They share geographic proximity, cultural ties, and overlapping markets. Yet their AI trajectories are diverging sharply. Understanding why Kenya and Ethiopia are moving faster, and what Somalia must do to catch up, provides crucial lessons for policymakers, business leaders, and regional institutions.
Kenya
Kenya’s relative lead in AI development is deliberate, built on policy positioning, a vibrant private sector, and international collaboration. The launch of Kenya’s National AI Strategy for 2025 to 2030 formalized the country’s commitment to integrating AI into national development planning. This strategy builds on years of digital transformation under Vision 2030 and a mature innovation ecosystem centered in Nairobi.
Innovation hubs such as iHub have nurtured startups, investors, and technologists for over a decade. The fintech revolution, led by M-Pesa, created both digital infrastructure and a culture of experimentation. These foundations have enabled AI startups to emerge in agritech, logistics, and financial risk modelling. Signvrse, a Kenyan startup using AI to recognize sign language, illustrates another key point. It demonstrates that Kenya’s AI ecosystem is not just importing solutions but generating innovations that address local challenges. Universities such as the University of Nairobi are producing graduates with data science and AI skills, while public-private partnerships expand digital literacy among civil servants and entrepreneurs. Collaboration with multinational firms and development agencies further strengthens Kenya’s technical and regulatory capacity.
Ethiopia
Ethiopia’s approach differs. It is more centralized and state-led but no less strategic. The establishment of the Ethiopian Artificial Intelligence Institute reflects a deliberate institutional commitment to AI at the national level. AI is embedded within Digital Ethiopia 2030, framing technology as a tool for economic modernization and state capacity. Universities such as Addis Ababa University are strengthening research and AI training pipelines. Government-backed innovation parks encourage applied research in agriculture, language processing, and public service optimization.
Ethiopia’s strategy reflects a political vision of AI as a tool for sovereignty and industrial catch-up. While the private sector is not as vibrant as Kenya’s, strong state coordination ensures that AI development aligns with national priorities.
Ethiopia demonstrates that structured, top-down institutionalization can be as effective as market-driven innovation, particularly when the goal is aligning technology with national development objectives.
Somalia
Somalia presents a paradox. It has one of Africa’s most innovative telecom and mobile money sectors. Private operators have built sophisticated digital payment systems in the absence of strong state institutions, and the population is digitally adaptive and entrepreneurial. Yet AI development remains fragmented.
Somalia lacks a formal national AI strategy, a dedicated governmental AI agency, and publicly funded research universities comparable to Ethiopia’s institutes. In other words, public and private universities alike face resource constraints that limit their capacity for advanced research. Infrastructure gaps, including limited broadband outside major cities and inconsistent electricity, further hinder progress.
Security challenges and political instability shape investment decisions. Long-term R&D requires predictable regulatory and economic environments, which remain limited. Education is another constraint. While Somali youth are tech-savvy, formal AI and data science programs are scarce. Talent development depends heavily on private bootcamps and self-learning.
Regulatory clarity is nascent. Data governance frameworks exist, but AI-specific ethical guidelines have yet to be formalized. Without certainty, private firms hesitate to scale AI solutions that rely on sensitive data.
The issue is not talent or ambition. It is the absence of coordination, strategic vision, and national prioritization.
What Somalia Must Do
Somalia’s starting point is not zero. Its telecom sector, fintech penetration, and entrepreneurial culture provide a strong base. The question is how to convert this into a structured AI ecosystem.
First, Somalia needs a clear national AI vision. A dedicated AI taskforce under the Office of the Prime Minister could draft a pragmatic roadmap outlining priority sectors, governance principles, and investment pathways.
Second, capacity building must be prioritized. Universities should partner with Kenyan and Ethiopian institutions to build joint research programs, scholarships, faculty exchanges, and virtual laboratories. This would accelerate knowledge transfer without large-scale infrastructure investment.
Third, the private sector must be mobilized. Telecom and fintech companies hold rich datasets. With regulatory safeguards, they could pioneer AI applications in credit scoring, fraud detection, logistics, and language technologies.
Fourth, infrastructure investment should prioritize digital foundations that enable AI rather than expensive high-performance computing.
Finally, regional collaboration is essential. Partnerships with Kenyan hubs and Ethiopian research institutes could establish a Horn of Africa AI corridor. Shared training programs and cross-border accelerators would reduce duplication and build scale.
Somalia’s leadership must frame AI as a strategic priority linked to economic resilience and sovereignty. Without political commitment, AI initiatives will remain fragmented and underfunded.
Hassan Mudane is a researcher and consultant specializing in AI-augmented workflows, public sector transformation, and human‑centered AI adoption. He is an advocate for the ethical use of AI and serves as the CEO of 26AI Consulting based in Mogadishu. He can be reached him via mudanep5@gmail.com