AI has moved from potential to proven economic value and while ambition setting is near universal across economies, execution speed is the differentiator, according to Boston Consulting Group’s The AI Maturity Matrix 2026: AI Is Transforming How Economies Develop and Compete report.
Execution speed is how quickly a government can turn strategy into coordinated action across the full economy and drive its transformation. Evidence across 75+ economies and 33 indicators shows governments play an important role in expediting AI adoption in the key sectors of their economy.
For governments, the question is no longer whether AI will affect their economies. It is how, and who, is positioned to capture the value.
Across Africa, attention is shifting from AI strategy to execution. Nigeria, Kenya, Morocco, Egypt, and South Africa are investing in the skills and infrastructure needed to support wider AI adoption and improve service delivery.
Readiness is rising everywhere, but not at the same speed
Nearly every economy in the study progressed in at least one dimension of BCG’s ASPIRE readiness framework: Ambition, Skills, Policy and regulation, Investment, Research and innovation, and Ecosystem.
Since the first AI Maturity Matrix in 2024, eight economies have moved up a readiness tier: Chile, the Czech Republic, Ecuador, Greece, Japan, Lithuania, Nigeria, and South Korea.
Japan and South Korea reached the top tier, joining the established AI Pioneers.
Japan paired AI friendly legislation with investments from hyperscalers, partnerships with national champions, and Anthropic’s first local presence in Asia-Pacific. Equally, South Korea has focused on domestic model development and ranked in the top five globally for notable AI models in Stanford HAI’s 2026 AI Index. It has directed large-scale public investment into compute and its wider AI ecosystem.
Nigeria is one of only eight economies globally to move up a readiness tier since the inaugural AI Maturity Matrix. The shift reflects progress in AI innovation, investment and efforts to develop solutions tailored to African languages and local market needs.
“The economies that advanced this year show that state action is what turns AI ambition into readiness,” says Joerg Hildebrandt, global leader of BCG’s Public Sector Practice and a co-author of the report. “The economies that will pull ahead are the ones that expedite the economic transformation in the key sectors of their economy. That requires accelerating on deep AI talent, as well as driving private sector investments.”
The report states that AI exposure today is greatest where AI is most capable – in sectors like information and communication services and financial services. As embodied AI advances and scales across humanoid robotics and industrial automation, more physical industries – like manufacturing, logistics, and agriculture – are expected to increase in exposure. Japan and China are focusing their frontier ambitions on physical AI, building on their strengths in manufacturing and industrial robotics.
Africa key findings at a glance
- Nigeria is one of only eight economies globally to move up a readiness tier since 2024.
- Financial services, communications, business services, and public services show the highest levels of AI exposure across leading African markets.
- Skills development remains the most significant barrier to faster AI adoption.
- Governments are increasingly using partnerships to expand infrastructure and access to AI technologies.
Africa’s opportunity lies less in competing to build frontier AI models and more in applying AI to economic and social challenges. The greatest gains are likely to come from higher productivity, more efficient public services, and broader access to economic opportunity.
How economies are building readiness
Four moves stand out among the economies making the fastest progress:
- Strategic interdependence through partnerships. AI sovereignty has moved from being an abstract concept to becoming a policy priority for individual nations. Sovereignty has four components (chips, compute and cloud, models, and applications), and owning all four is exceedingly rare. The key question for economies is which dependencies to deliberately maintain – and which they are content to source through partnership. Partnerships with global cloud, chip, and AI model providers deliver capability with speed. Singapore sources frontier model capabilities and focuses its own resources on adoption. The UAE hosts large-scale AI infrastructure built with US partners; Saudi Arabia pairs a national AI champion with global chip and cloud partnerships; and Nigeria has built local-language AI on openly published models.
- Targeted efforts on removing bottlenecks to AI infrastructure. Compute remains heavily concentrated, with the US far ahead, but investment is flowing into compute and the power and connectivity behind it. The UK has set ambitious public compute targets and designated zones with better access to power and planning approvals. Germany shares supercomputing capacity across the EU; France backs a national champion with dedicated compute; and the US is fast-tracking data centres and their grid connections.
- Investment in attracting and building AI talent. Talent is emerging as a key constraint on how fast readiness can improve. Cross-border relocation of highly skilled AI professionals fell 12% in 2025. India illustrates the alternative route, building its position on a large pool of AI specialists and broader workforce training and access through AI labs in smaller cities.
- Setting clear policy that attracts private investment. Countries now use policy to attract investment as much as to constrain risk – and the clarity of position matters more than degree of openness. In 2026, we see both minimal-constraint regimes and oversight-based regimes. Even in the most open regimes, safeguards on transparency, redress, and human oversight are becoming a condition of sustained adoption.
From deployment to impact
“Governments need to rethink economic policy around AI rather than treating it as just another technology,” says Christian Schwaerzler, chair of BCG’s Centre for Public Economics and a co-author of the report. “If AI reshapes work and businesses faster than economies adapt, the tax base, education systems, and safety nets come under pressure at the same time.”
Across Africa, expanding access to digital and AI skills will be critical if countries are to realise the full value of the technology.
“Coordinating cross-government action to translate strategy to policy is only the first step,” says Steven Mills, chief AI ethics officer and a co-author of the report. “The biggest test has not yet arrived. Governments now need to manage the societal impact of AI including workforce disruption, trust in government, and resource demands. Nations must act now to ensure a safe and positive AI transition as they continue accelerating AI within their economies.”
For Africa, the priority now is delivery. Countries that can translate investment in skills and infrastructure into wider adoption across their economies will be best placed to capture the benefits of AI.