The global contest over Artificial Intelligence (AI) governance is rapidly emerging as one of the defining political economy questions of the decade. Beneath the technical language of algorithms, standards, and safety frameworks lies a deeper struggle over power, sovereignty, market control, and regulatory influence. Increasingly, the debate is no longer simply about how AI should be governed, but who gets to define the rules, whose values are embedded in those rules, and whether developing economies will participate as co-authors or merely as downstream implementers of governance systems designed elsewhere.
The recent Lawyers Hub Africa–Europe Cooperation on AI Governance Report situates Africa at the centre of this emerging geopolitical realignment. The report frames the global AI transition as a modern technological scramble in which Africa has become essential to data generation, model training, digital labour, and market expansion, while remaining structurally underrepresented in the institutions that develop global governance standards. The result is an increasingly asymmetrical ecosystem in which the continent participates heavily in AI deployment but exercises limited authorship over the legal and institutional frameworks that shape its future.
Different Jurisdictions’ Approaches to AI Governance
Across major jurisdictions, divergent governance philosophies are beginning to crystallise.
The United States has increasingly adopted an innovation-first and competitiveness-driven posture. Through the White House’s America’s AI Action Plan and related executive actions, AI is framed primarily as a strategic and geopolitical capability tied to national security, industrial competitiveness, and technological leadership. The central policy concern is not merely AI safety, but the preservation of American dominance in frontier technologies. Consequently, the regulatory philosophy emerging from Washington prioritises reducing regulatory friction. It prioritises accelerating infrastructure deployment, strengthening compute capacity, and avoiding what policymakers describe as bureaucratic constraints that could slow innovation.
Similarly, the United Kingdom has deliberately resisted the creation of a single omnibus AI regulator or a comprehensive Artificial Intelligence Act. Instead, its governance framework, articulated through the UK AI Regulation White Paper, advances a proportionate and pro-innovation model anchored on the principle of regulating the application of AI rather than the technology itself. The UK model relies heavily on existing sector regulators and outcome-based governance, reflecting a broader preference for agile and context-sensitive regulation over rigid statutory categorisation.
Singapore has adopted a similarly pragmatic approach through its Model AI Governance Framework. Rather than imposing legally binding classifications, Singapore’s framework is voluntary, operational, and governance-oriented. It emphasises accountability, explainability, human-centricity, and practical implementation safeguards while preserving flexibility for innovation and commercial scalability. Importantly, the country assesses AI systems largely through deployment context and governance controls rather than fixed legal risk categories.
This stands in contrast to the European Union (EU) AI Act, which represents the most comprehensive and legally prescriptive AI governance regime globally. The EU model is fundamentally risk-based, categorising AI systems into prohibited, high-risk, limited-risk, and minimal-risk tiers, each attracting varying degrees of compliance obligations. Through what scholars increasingly describe as the “Brussels Effect”, the EU’s regulatory architecture effectively exports its standards globally, as firms seeking access to European markets must comply irrespective of their country of origin.
However, even within Europe, concerns are emerging regarding the operational and economic burdens associated with highly prescriptive regulation. Recent developments surrounding the proposed Digital AI Omnibus reforms suggest growing recognition that certain compliance obligations may be more onerous than initially anticipated, particularly for startups, SMEs, and emerging technology ecosystems. For African firms operating within resource-constrained environments, the cost of compliance with highly technical conformity, auditing, documentation, and monitoring requirements may become structurally exclusionary rather than merely regulatory.
Africa’s Balancing Act
It is within this global context that African concerns around digital sovereignty and data governance are becoming increasingly prominent.
The Lawyers Hub report identifies “data colonialism” as one of the defining governance challenges confronting the continent. Across sectors such as agriculture, fintech, health, and digital commerce, African-generated data is frequently processed, stored, monetised, and governed through foreign-owned infrastructure, cloud ecosystems, and contractual frameworks designed outside the continent. The economic value extracted from these data ecosystems often accrues disproportionately to external technology actors despite originating from African users, workers, and institutions.
Kenya’s response to these emerging asymmetries is increasingly reflected in the Kenya National Artificial Intelligence Strategy 2025-2030, which adopts a distinctly sovereignty-conscious and development-oriented framing. The Strategy recognises AI not merely as an emerging technology issue, but as a broader economic transformation question linked to industrialisation, public service delivery, workforce transition, digital inclusion, and long-term national competitiveness.
Central to the Strategy is the adoption of a “Local First” orientation. The framework emphasises strengthening domestic innovation ecosystems, local talent development, African-language datasets, sovereign digital infrastructure, and locally responsive AI deployment models. This reflects an explicit policy concern that, without deliberate intervention, African economies risk remaining perpetual consumers of externally developed AI systems rather than meaningful participants in value creation.
The governance tensions identified in the Strategy are increasingly visible within ongoing legislative discussions surrounding the proposed Kenyan Artificial Intelligence Bill, 2026. Much like broader global debates, Kenya is attempting to navigate the difficult balance between enabling innovation and establishing meaningful safeguards around accountability, transparency, labour disruption, automated decision-making, and public trust.
One of the most contested questions concerns whether Kenya should adopt a heavily centralised risk-classification regime similar to the EU model or pursue a more flexible, sector-sensitive governance architecture akin to the UK or Singapore approaches. Stakeholders have raised concerns that overly rigid technology-based classifications, duplicative licensing obligations, or broad criminal penalties could unintentionally suppress innovation within an emerging ecosystem that remains comparatively young but rapidly expanding.
At the same time, the Bill reflects growing awareness that AI governance cannot be divorced from labour market transformation and broader socio-economic realities. Provisions relating to workforce impact assessments, reskilling obligations, and human oversight mechanisms mirror international discussions around responsible automation and human-centric governance. These concerns are particularly relevant for Kenya, given its growing role within the global digital labour economy, including data annotation, content moderation, and AI support services.
The deeper challenge, however, extends beyond legislation itself and into questions of institutional capability.
The Lawyers Hub report highlights a significant asymmetry between African regulators and their counterparts in developed economies. While many African countries have enacted data protection legislation and established regulatory authorities, these institutions frequently operate with limited technical capacity, constrained enforcement resources, and comparatively modest budgets. In contrast, regulators in Europe and North America often possess substantially larger institutional ecosystems capable of conducting advanced technical audits, enforcement actions, participation in standards, and sustained policy development.
This imbalance has material consequences. It affects Africa’s ability to shape global standards, negotiate equitable partnerships with hyperscalers and multinational technology firms, conduct independent algorithmic audits, and develop locally grounded governance frameworks responsive to African socio-economic conditions.
Consequently, the emerging governance debate is no longer simply about regulation. It is fundamentally about regulatory sovereignty, institutional capability, and economic positioning within the next phase of the digital economy.
The Lawyers Hub report, therefore, proposes a shift from passive policy adoption to active co-governance. Among the proposals advanced are stronger AU–EU regulatory dialogue mechanisms, African-led AI research funding structures, regional governance coordination, investment in African-language models, electoral integrity safeguards against AI-enabled disinformation, and public-private investment in workforce transition and digital skills development.
Conclusion
Ultimately, the future of AI governance may depend less on whether jurisdictions adopt strict or flexible regulatory models and more on whether governance frameworks can simultaneously achieve legitimacy, adaptability, innovation, and public trust.
For Kenya and much of Africa, the central policy question is therefore not whether AI should be governed, but how governance can avoid reproducing historical patterns of technological dependency. The objective increasingly appears to be the construction of an agile, context-sensitive, and sovereignty-conscious governance framework capable of protecting rights and public interests without undermining innovation, competitiveness, or local participation in the emerging AI economy.
The global AI transition is already reshaping the architecture of economic and political power. The jurisdictions that successfully balance innovation, accountability, institutional capacity, and digital sovereignty will likely determine not only the future of AI governance but also their broader position within the next global economic order.