Data Explorer
Your next AI bet is
a geography problem.
Talent costs, infra maturity, and regulatory readiness vary wildly across borders. This map scores 193 countries on the factors that actually predict where AI investment pays off.
| # | Country↕ | Region↕ | Score▼ | vs '24↕ | Stage↕ |
|---|---|---|---|---|---|
| 1 | United States | North America | 87.7 | +17.8 | 4 |
| 2 | Japan | East Asia & Pacific | 86.1 | +11.8 | 4 |
| 3 | United Kingdom | Europe & Central Asia | 84.0 | +17.3 | 4 |
| 4 | Germany | Europe & Central Asia | 84.0 | +12.5 | 4 |
| 5 | South Korea | East Asia & Pacific | 83.6 | +9.8 | 4 |
| 6 | Canada | North America | 82.9 | +11.8 | 4 |
| 7 | Australia | East Asia & Pacific | 82.4 | +9.1 | 4 |
| 8 | Netherlands | Europe & Central Asia | 81.6 | +11.7 | 4 |
| 9 | Denmark | Europe & Central Asia | 81.2 | +6.6 | 4 |
| 10 | Sweden | Europe & Central Asia | 80.9 | -6.5 | 4 |
| 11 | Spain | Europe & Central Asia | 80.9 | +15.3 | 4 |
| 12 | Singapore | East Asia & Pacific | 80.7 | +6.2 | 4 |
| 13 | France | Europe & Central Asia | 80.6 | -0.0 | 4 |
| 14 | Italy | Europe & Central Asia | 80.1 | +8.9 | 4 |
| 15 | Finland | Europe & Central Asia | 79.8 | +0.4 | 4 |
| 16 | Norway | Europe & Central Asia | 79.7 | -8.9 | 4 |
| 17 | Ireland | Europe & Central Asia | 78.7 | +9.0 | 4 |
| 18 | Austria | Europe & Central Asia | 77.8 | -8.2 | 4 |
| 19 | China | East Asia & Pacific | 77.1 | +6.4 | 4 |
| 20 | Poland | Europe & Central Asia | 76.5 | +13.1 | 4 |
United States
North America
87.7
+17.8
Japan
East Asia & Pacific
86.1
+11.8
United Kingdom
Europe & Central Asia
84.0
+17.3
Germany
Europe & Central Asia
84.0
+12.5
South Korea
East Asia & Pacific
83.6
+9.8
Canada
North America
82.9
+11.8
Australia
East Asia & Pacific
82.4
+9.1
Netherlands
Europe & Central Asia
81.6
+11.7
Denmark
Europe & Central Asia
81.2
+6.6
Sweden
Europe & Central Asia
80.9
-6.5
Spain
Europe & Central Asia
80.9
+15.3
Singapore
East Asia & Pacific
80.7
+6.2
France
Europe & Central Asia
80.6
-0.0
Italy
Europe & Central Asia
80.1
+8.9
Finland
Europe & Central Asia
79.8
+0.4
Norway
Europe & Central Asia
79.7
-8.9
Ireland
Europe & Central Asia
78.7
+9.0
Austria
Europe & Central Asia
77.8
-8.2
China
East Asia & Pacific
77.1
+6.4
Poland
Europe & Central Asia
76.5
+13.1
Source: Agence Française de Développement — AIIPI 2026 (CC-BY)
What the data says
Spending alone does not buy AI readiness. The countries that score highest invest in foundations — connectivity, talent pipelines, and institutions — not just compute.
6:1
The readiness gap is wider than the wealth gap
USA at 87.7, South Sudan at 14.2. If you are choosing where to deploy AI workloads, the infrastructure floor matters more than the GDP ceiling.
#19
Money cannot buy what China is missing
Billions in AI spending, yet ranked behind Poland. The index penalizes closed data ecosystems and weak inclusion — a warning for any top-down AI strategy.
9 / 15
Europe's quiet advantage
Nine of the top fifteen are European. Strong universities, high connectivity, and regulatory clarity beat raw VC volume. Your nearshore AI team might belong in Lisbon, not Palo Alto.
#28
India has the talent. Not the floor.
World-class engineers, but the index looks at the whole country — inclusion, connectivity, institutions. Great for outsourcing AI development. Harder for broad AI deployment.
~100
The "almost ready" trap
Half the world scores between 45 and 75 — prepared on paper, stalled in practice. The gap between "can pilot AI" and "can scale AI" is where most investment dies.
#52
Africa's leapfrog candidates
South Africa, Morocco, and Tunisia lead the continent. Mobile-first infrastructure and young populations create an AI adoption path that skips legacy IT entirely.
Source: AI Investment Potential Index (AIIPI) 2026, Agence Française de Développement. Published on data.gouv.fr under CC-BY.
What actually predicts AI success
The AIIPI trains machine-learning models against real AI investment flows — VC rounds, private equity, and M&A — then ranks which indicators best predict where capital lands. The results challenge common assumptions.
Research output
The number of AI research articles published by a country's institutions is the single strongest predictor of investment. Not patents, not VC dollars — published research.
Government effectiveness
Institutional quality and policy implementation capacity matter more than the policies themselves. A well-run government predicts AI investment better than having an AI strategy on paper.
Data privacy & protection
Countries with strong data protection frameworks attract more AI investment, not less. Regulatory clarity builds the trust that enterprise AI deployments require.
Mobile connectivity
The GSMA Connectivity Index — measuring digital accessibility for citizens — outweighs raw broadband speed. AI scales where the population can actually access it.
Population
Market size still matters. Large domestic populations represent scalability and addressable market — the scope for AI technologies to generate returns.
Variable importance derived from Random Forest model trained on AI investment counts (VC, PE, and M&A), validated via 10-fold cross-validation. Source: AIIPI 2025 methodology paper.
Regional benchmarks
Average AIIPI scores by World Bank region. The gap between North America and Sub-Saharan Africa is not just a wealth story — it tracks infrastructure, governance, and data maturity.
Regional averages from AIIPI 2025. Individual country scores available in the interactive map above.
How the index works
The AIIPI is published annually by the Agence Française de Développement (AFD), France's public development bank. It scores 193 countries across 19 indicators and 6 dimensions, using machine-learning models trained on real AI investment flows to determine what actually predicts where capital lands.
01
Economic environment
Market size, economic prosperity, energy access, and the sophistication of a country's productive structure.
02
Governance
Democratic freedoms, institutional quality, policy implementation capacity, and investment climate stability.
03
Digital & physical infrastructure
Mobile connectivity, broadband maturity, and telecommunications networks that enable AI deployment at scale.
04
Human capital
Workforce skills, education levels, and the country's capacity for knowledge generation and AI research.
05
Data governance
Government commitment to AI strategy and the strength of data privacy and protection frameworks.
06
Statistical performance
The quality of a nation's statistical system — how well it produces, manages, and shares data.
ML-derived weighting
Weights are not assigned by committee. The AIIPI trains Random Forest, XGBoost, Elastic Net, and Linear Regression models against actual AI investment flows, then derives feature importance from the best performer (Random Forest, validated via 10-fold cross-validation). Indicators that better predict real investment carry more weight.
Four investment stages
Advanced AI ecosystems with exceptional investment potential.
Solid foundations, but gaps in one or more dimensions.
Emerging capabilities. Targeted investment can unlock potential.
Foundational gaps in infrastructure, governance, or human capital.