Public Sector AI Readiness: The Case of Sub-Saharan Africa

AI advancements have generated both excitement and numerous opportunities for transformation across various sectors in unexpected ways. Interestingly, one often overlooked area of potential impact is the public sector. However, successfully integrating and utilizing AI in this crucial domain hinges on a nation's readiness to effectively embrace and harness this powerful technology—an observation that may sound cliché but is undeniably true.

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Artificial Intelligence (AI) has emerged as a transformative force across various sectors, with the public sector standing to gain significantly from its integration. The potential of AI to revolutionize government operations and public service delivery is immense, offering a wide array of benefits that could address longstanding challenges in efficiency, effectiveness, and citizen engagement.

In the realm of public administration, AI presents numerous application areas that could reshape how governments function and interact with citizens. Automated administrative processes can streamline routine tasks, reducing bureaucratic bottlenecks and freeing up human resources for more complex, value-added activities. AI's capacity for enhanced decision-making, by analyzing vast amounts of data, can provide crucial insights to support evidence-based policymaking and resource allocation.

The technology also enables personalized citizen services, tailoring public services to individual needs and thereby improving citizen satisfaction and engagement. Through predictive analytics, governments can leverage AI to forecast trends, anticipate needs, and proactively address potential issues in areas such as public health, urban planning, and disaster management.

AI has the potential to revolutionize public safety, enhancing law enforcement and emergency response capabilities through advanced surveillance, real-time data analysis, and predictive policing. In the realm of resource management, AI can optimize the allocation and utilization of public resources, from energy grids to transportation systems, leading to more sustainable and efficient cities. Additionally, AI algorithms can be employed for fraud detection and prevention, identifying patterns and anomalies to combat fraud in areas such as taxation and public procurement.

While the potential benefits of AI in the public sector are substantial, realizing these advantages requires careful consideration of factors such as technological infrastructure, data availability, governance frameworks, and human capital development. As governments worldwide explore AI integration. However first and foremost there needs to be the assessment of their readiness which becomes crucial for developing effective strategies to harness this technology's full potential.

The recently launched ‘Government AI readiness Index 2023’ report by Oxford Insights, outlines a framework for assessing a given government’s AI readiness. The framework consists of three pillars, namely, technology, government and data infrastructure. Each of these pillars has additional dimensions: technology comprises maturity, innovation and human capital; government comprises vision, digital capacity, and, governance and ethics; and, data and infrastructure encompass infrastructure, data availability and data representativeness. Additionally, data on these pillars and dimensions is provided for each of the 193 countries featured in the report. While the report highlights global and regional trends in AI readiness, SSA included, trend perspectives in each of the SSA regions, which are equally important, are not provided. Using AI readiness data from Oxford insights, this article explores the trends in AI readiness within each of the four SSA regions : Eastern, Western, Central and Southern Africa.

AI readiness index data: overview

One hundred and ninety-three countries, from seven regions, are featured in the Government AI readiness Index. The regional breakdown of countries by number and percentage, based on the World Bank regional classification criteria is as follows : East Asia & Pacific (29,15%), Europe & Central Asia (52,26.94%), Latin America & Caribbean (33,17.1%),Middle East & North Africa (21,10.88%),North America (2),South Asia (8,4.15%) and SSA ( 48,24.87%).Majority of countries are based in Europe & Central Asia, followed by SSA. South Asia and North America, on the other hand ,have the lowest country composition.

An analysis of the world by income, based on the World Bank income classification criteria, is presented in Figure 2. According to the classification, there are four categories: High Income, Lower middle income, Upper middle income and Low income. Most countries fall within three categories: High income, Upper middle income and Low income. All countries are classified by income except Venezuela. Venezuela, previously considered an upper-middle income country, is currently unclassified due to data unavailability.

Data Insights

Global insights

A global overview of AI readiness can be seen in Figure There is considerable variability in the scores, as evidenced by the 75-point difference between the lowest-ranked country, the Democratic People’s Republic of Korea, and the highest-ranked country, the United States of America. It is also observed that most low-ranking countries are based in Sub-Saharan Africa, whilst most high-ranking countries are based in North America and Europe and Central Asia regions.

An analysis of AI readiness by region, based on average scores, is presented in Figure 4. The order of AI readiness, starting from the most to the least prepared, is as follows: North America, Europe and Central Asia, East Asia and Pacific, Middle East and North Africa, Latin America & Caribbean, South Asia and Sub-Saharan Africa. Additionally, these scores vary considerably across regions. The highest ranked-region, North America, has an average score of (80.93) compared to the lowest-ranked region, SSA, with a score of 30.15.

Sub-Saharan Africa

Forty-eight countries are included in the analysis of Sub-Saharan Africa. Mauritius is the highest-ranked country scoring 53.26 points, whilst South Sudan ranks lowest with a score of 18.26 points, demonstrating significant disparities within the region (Fig 6). Although Mauritius ranks highly in Africa, its global ranking (61) indicates that much needs to be done with respect to accelerating AI readiness.

Other high-ranking countries in the top-five category include South Africa (second, 47.28), Rwanda (third ,45.39), Senegal (fourth, 42.58) and Benin (41.37) (Figure 7).

Aside from South Sudan, countries in the bottom-five category include Eritrea (19.62), Central African Republic (19.74), Burundi (20.87) and the Democratic republic of the Congo (21.47) (Figure 8).

Eastern Africa

Mauritius, being the regional leader, takes the lead in Eastern Africa as well. Rwanda, Kenya, Seychelles and Uganda come in second, third, fourth and fifth respectively. Although Uganda ranks fifth, Tanzania and Ethiopia are not far behind, scoring 32.86 and 32.59 points respectively. Two out of the lowest-five ranked countries in SSA are located in this region: Eritrea and South Sudan. Substantial disparity exists not only between the highest and lowest-ranked countries in this region but also within the top-five category. Mauritius is 35 points ahead of South Sudan, the lowest-ranked country in Eastern Africa and SSA, and 20 points ahead of Uganda, the fifth-ranking country in Eastern Africa.

West Africa

Senegal takes a marginal lead in West Africa, scoring 42.58 points, closely followed by Benin at 41.37 and Nigeria at 39.88. Liberia at 22.24 points, is the lowest-ranking country in the region. Other countries in the lowest-five category include Guinea Bissau, Niger, Sierra Leone and Mauritania. Compared to Eastern Africa, the disparity in scores between the highest and lowest-ranked country in West Africa is slightly less, with Senegal taking a 20-point lead against Liberia.

Central Africa

Gabon takes the lead in Central Africa, scoring 33 points 12 points ahead of the Central African Republic, the lowest-ranked country in this region. Although the disparity between the lowest and highest-ranked countries is much less in this region, the readiness of the region overall is quite low compared to other regions.

Southern Africa

South Africa takes the lead in Southern Africa, scoring 47.29 points, whilst Malawi at 24.87, takes the lowest-ranking position. Substantial disparity exists between the two countries in terms of ranking, with South Africa taking a 23-point lead. Botswana and Namibia, considered strong economies within the region, follow closely behind South Africa at 38.84 and 35.37 points respectively.

Conclusion

Although AI has the potential to transform the public sector in Africa, many countries in SSA are not well positioned to benefit from the technology, as evidenced by the low index scores. Significant investments in data, governance and technology are crucial to advancing the integration of AI into public service delivery. Future articles will delve into each pillar, and evaluate public sector readiness with respect to technology, infrastructure and governance, using the indicators provided in the data.