Africa is adopting artificial intelligence faster than almost any region on earth. According to a 2025 Google and Ipsos study, 93 percent of Nigerians already use AI tools to learn, work, and explore business ideas – a figure that surpasses the global average by nearly 20 percentage points. Yet while adoption accelerates at breakneck speed, the governance frameworks being discussed across the continent remain largely borrowed from Western templates: the EU AI Act, the US executive order on AI, or OECD guidelines designed for economies and institutions that look nothing like those in Lagos, Nairobi, or Accra.
This creates a dangerous mismatch. The systems being built and deployed are operating in societies where the assumptions underlying Western governance models – formal labour markets, centralised regulatory capacity, mature data infrastructure, high digital literacy – simply do not hold. Africa does not need a translated version of someone else’s rulebook. It needs governance architecture designed from the ground up for its own realities.
The adoption-governance gap
There is a critical distinction between using AI tools and governing AI systems. Across Africa, millions of people are enthusiastically using AI-powered applications for everything from language translation and content creation to financial analysis and agricultural planning. This is remarkable progress. But enthusiasm without institutional architecture creates fragmented outcomes: duplicated investments across government ministries, conflicting standards between regulators, and a growing dependence on foreign-built systems that were never designed with African contexts in mind.
Strategic AI governance goes beyond regulation. It means building governance frameworks that account for informal economies where most Africans earn their livelihoods, oral cultures where trust is built through community networks rather than written contracts, multilingual populations navigating digital systems often built in a single language, and trust dynamics shaped by decades of institutional disappointment.
Why Western frameworks fall short
The EU AI Act was designed for a specific context. It assumes formal employment relationships where AI-related labour displacement can be tracked through established safety nets. It assumes centralised data registries where algorithmic systems can be audited against standardised criteria. It assumes regulatory bodies with the technical capacity and political independence to enforce compliance across complex technology ecosystems. These assumptions describe Europe. They do not describe most of Africa.
Localisation of AI governance is not a matter of translating documents or adjusting terminology. It requires rethinking the entire governance architecture. What works in Brussels may actively harm in Abuja – not because the principles are wrong, but because the implementation pathways assume institutional realities that do not exist.
Behavioural economics as the missing governance layer
Most AI governance discussions focus on technical standards, ethical principles, and regulatory mechanisms. What they consistently overlook is the behavioural dimension: how do people actually interact with AI systems, and what determines whether they trust, adopt, or reject these technologies? Behavioural intelligence – the systematic understanding of decision-making patterns, trust formation, incentive structures, and cultural norms – is the bridge between AI capability and AI adoption.
A four-pillar framework for African AI governance
I propose a practical framework built on four pillars. The first is local context assessment – rigorous analysis of economic, social, cultural, and institutional conditions before any governance framework is drafted. The second is behavioural alignment – testing every mechanism against how people actually behave, not how policymakers assume they behave. The third is multi-stakeholder co-design – building frameworks with the participation of communities they affect. The fourth is sovereignty-first architecture – prioritising governance capacity that ensures long-term autonomy over AI systems.
The path forward
The question facing African leaders is not whether to govern AI – it is whether to govern it on their own terms or on terms designed for someone else’s context. Africa has the talent, the creativity, and the urgency to build something better. The next chapter of Africa’s digital story should be authored by Africa – not translated from a foreign text.
David Adeoye Abodunrin is a futurist, AI transformations coach, and author of Fintech Black Box. He advises governments and institutions on strategic AI governance, behavioural intelligence, and digital transformation. Book a consultation →
Frequently asked questions
What is AI governance in Africa?
AI governance in Africa refers to the frameworks, policies, and institutional mechanisms that guide how artificial intelligence is developed, deployed, and regulated across the continent, accounting for informal economies, diverse cultural contexts, and varying institutional capacity.
Why can’t African countries simply adopt the EU AI Act?
The EU AI Act was designed for formalised economies with centralised regulatory capacity. African economies are characterised by large informal sectors and different trust dynamics, meaning governance frameworks must be built for these specific realities.
What role does behavioural economics play in AI governance?
Behavioural economics helps policymakers understand how people actually interact with AI systems – what drives trust, adoption, or rejection – making governance frameworks practically effective rather than just technically sound.