Most AI pilots in banking never leave the sandbox, not because the models aren't good enough, but because governance, data readiness and integration were never designed in. We build for production from the first use case.
| Country | per 1 million people | Year |
|---|---|---|
| South Africa | 16,658 | 2024 |
| Tunisia | 912.3 | 2024 |
| Morocco | 577.3 | 2024 |
| Botswana | 471.2 | 2024 |
| Namibia | 427.4 | 2024 |
| Kenya | 399.7 | 2024 |
| Libya | 388.6 | 2024 |
| Rwanda | 161.0 | 2024 |
| Algeria | 119.3 | 2024 |
| Swaziland | 116.7 | 2024 |
| Djibouti | 114.7 | 2024 |
| Togo | 101.9 | 2024 |
| Nigeria | 98.4 | 2024 |
| Zimbabwe | 90.0 | 2024 |
| Gabon | 87.0 | 2024 |
| Lesotho | 86.0 | 2024 |
| Egypt | 82.2 | 2024 |
| Zambia | 80.5 | 2024 |
| Equatorial Guinea | 66.6 | 2024 |
| Ivory Coast | 60.8 | 2024 |
| Senegal | 59.6 | 2024 |
| Gambia | 58.7 | 2024 |
| Uganda | 53.8 | 2024 |
| Tanzania | 52.4 | 2024 |
| Mauritania | 52.2 | 2024 |
| Ghana | 48.9 | 2024 |
| Angola | 48.5 | 2024 |
| Mozambique | 37.2 | 2024 |
| Malawi | 32.5 | 2024 |
| Burkina Faso | 28.8 | 2024 |
| Cameroon | 25.2 | 2024 |
| Madagascar | 25.1 | 2024 |
| Ethiopia | 23.7 | 2024 |
| Mali | 21.0 | 2024 |
| Benin | 20.4 | 2024 |
| Republic of the Congo | 16.6 | 2024 |
| Somalia | 14.0 | 2024 |
| Liberia | 13.0 | 2024 |
| Guinea | 12.3 | 2024 |
| Sierra Leone | 11.3 | 2024 |
| Guinea Bissau | 10.9 | 2024 |
| Burundi | 10.6 | 2024 |
| Democratic Republic of the Congo | 9.7 | 2024 |
| South Sudan | 5.3 | 2024 |
| Niger | 5.2 | 2024 |
| Sudan | 2.4 | 2024 |
| Eritrea | 2.0 | 2024 |
| Central African Republic | 1.7 | 2024 |
| Chad | 1.2 | 2024 |
No World Bank figure for this indicator: Western Sahara, Somaliland.
Figures are the most recent available per country, which is why some years differ. Retrieved from the World Bank Open Data API.
We work best with teams who've already tried an AI proof of concept, learned something from it, and now want a real path to production, with governance a regulator will actually accept.
Structured evaluation of where AI creates real value in your business, ranked by impact and feasibility, not hype.
Model, hosting and integration decisions weighed against cost, data residency and regulatory constraints.
Risk, model oversight and audit frameworks built to satisfy your regulator, not just your engineering team.
Hands-on delivery that takes a validated use case from pilot through to a monitored production deployment.
Use-case mapping and data readiness assessment, ranked against value and regulatory risk.
Architecture, model selection and governance framework designed together, not bolted on afterward.
A tightly scoped pilot with clear success criteria and a real path to production from day one.
Production rollout, monitoring and internal capability transfer so AI becomes a standing capability, not a project.
Every engagement includes a risk and oversight framework a regulator will actually accept, not an afterthought once a pilot works.
Model and vendor decisions are made on merit, not lock-in: your architecture should outlast any single provider.
Pilots are scoped with a real path to production, so a proof of concept doesn't stall in evaluation indefinitely.
We can explain the AI strategy in the same language the rest of the business already uses.
Tell us what you've tried so far, and we'll tell you honestly what's missing to take it further.