AI is transforming the world and Africa is no exception, governments on the continent need to integrate AI into the public sector and create an environment that fosters its use by the private sector.
Learn about the 25 indicators used in the African AI Index Report a comprehensive framework for governments on the continent to assess national AI readiness.
One might ask why not use the current AI readiness tools, it is simple, they require data sets unavailable on the African continent.
We selected 25 AI readiness indicators whose data is available in all 54 countries on the African continent. Call our African AI Index a more contextual, and practical AI readiness framework African for governments.
The 25 AI Readiness Indicators are categorized into 5 thematic areas: AI Regulations, AI infrastructure, AI Skills, AI Innovation, and Government AI initiatives.
AI Regulations | AI Infrastructure | AI Skills | AI Innovation | Government AI initiatives |
AI legislation | Electricity access rate | Computer Science graduates per year | AI start-ups | e-Government |
Data protection and privacy legislation | Internet penetration rates | AI graduates per year | AI research papers published per year | Government AI research funding |
National AI Strategy | Cost of data bundles | Expert AI Groups | AI patents per year | AI research labs & hubs |
Cybersecurity legislation | Computer access per household | AI job ads per year | Academic-Business partnerships | Open government data |
AI regulatory body | Smartphone penetration rate | Computer literacy | AI start-up incubators and accelerators | AI literacy in the public sector |
Computers per student | Female computer science graduates | Access to Capital | AI public-private partnerships |
Outlier AI Readiness Indicators that we excluded
Using Oxford Insights 2023 Government AI Readiness Index as a benchmark find a full list of the AI readiness indicators we didn’t include and our logic for excluding them.
AI Readiness Indicator | Reason for exclusion |
Supercomputers | You don’t need supercomputers for AI |
Number of non-AI technology unicorns | Unicorns represent a tiny proportion of overall entrepreneurial activity |
Value of trade in ICT good and services (per capita) | No direct correlation with AI readiness |
Company investment in emerging technology | Unreliable data on this indicator |
Number of AI unicorns | Unicorns represent a tiny proportion of overall entrepreneurial activity |
Regulatory quality | Difficult to measure |
National ethics framework (Y/N) | Captured under AI legislation |
Accountability | No direct correlation with AI readiness |
Government effectiveness | Difficult to measure and very subjective |
Government’s responsiveness to change | Difficult to measure |
GitHub users per thousand population | Unreliable data |
Quality of engineering and technology higher education | Subjective datasets used for this indicator |
Graduates in STEM | A generic indicator that can be better captured by referencing more direct skills |
Gender gap in Internet access | Data on this indicator is not available at scale in Africa |
Cost of internet-enabled device relative to GDP per capita | A generic indicator that can be better captured by using more specific data points. |
Statistical capacity | No direct correlation with AI readiness |
Data governance | Captured under AI legislation |
Adoption of emerging technologies | Difficult to measure |
5G infrastructure | Indirectly captured by Internet speeds |
Time spent dealing with government regulations | Too generic |
This research project happened by accident when I wrote an article on the African Union launching a Continental AI Strategy which led to another article Comparing African AI regulations and the rest as they say is history.
I will write follow-up articles to provide more details about the AI Readiness indicators, scores, weighting, and calculations for the African AI Index culminating in a full report that will be published annually.
Here’s to Africa realizing the full benefits of AI!