Artificial Intelligence (AI) is transforming businesses and economic activities worldwide through its capacity to mimic or replicate human-like intelligence. With the growing potential of AI, many countries are adopting various strategies to become AI-ready. According to PricewaterhouseCoopers (PwC) analysis published in 2017, AI is expected to contribute about $15.7 trillion to the global economy by 2030. AI readiness involves the capacity of a country or organisation to use AI technologies to effectively drive economic growth, social development, and overall welfare. However, AI readiness is still challenging in many African countries due to limited AI-supportive facilities and infrastructure. The availability of reliable electricity, poor internet connectivity, and computing power are vital tools for AI development, but many African countries still need to improve in these areas. It is, however, essential to note that a country’s readiness for AI is not simply a question of preparing to buy and install new technologies. The transformative nature of AI typically calls for preparation across multiple critical areas. To capture AI’s potential to create value, governments and organisations need to retool and rework their existing processes, upskill or hire key talents, refine approaches toward partnership, and develop the necessary data and technical infrastructure to deploy these advanced technologies. There are fundamental AI readiness pillars that need to be put in place by governments and organisations to enhance and create a conducive environment for AI to thrive.
Fundamental Pillars for AI Readiness
According to Oxford Insight (2021), the AI readiness of a nation anchors on three main pillars that capture and show a country’s government’s readiness to implement AI in public service delivery and support innovation in the private sector. These fundamental pillars include:
- The Technology Sector Pillar: The technology sector of a country plays a crucial role in implementing AI strategies as the government depends on a good supply of AI tools from its technology sector, which needs to be competitive and dynamic in size. This sector should have high innovation capacity, underpinned by a business environment that supports entrepreneurship and a good flow of Research and Development (R&D) spending. In addition, the skills and education of the people working in this sector are critical as the level and quality of human capital will determine AI productivity level.
- The Data and Infrastructure Pillar: A country’s infrastructure and data capacity goes a long way in determining its AI readiness. AI tools require lots of high-quality available data, which should represent all citizens within a given country (data representativeness) to avoid bias and error. Hence, to achieve this data potential, necessary infrastructure must be in place to power AI tools and deliver them to citizens.
- The Government Pillar: This is the most important as the other pillars become dormant without a government’s interest and desire to use AI for transformative purposes. The government should have a strategic vision for developing and managing AI, supported by appropriate regulation and attention to ethical problems (governance & ethics). Furthermore, it must have a robust internal digital capacity, including the skills and practices that support its adaptability in the face of new technologies.
Figure 1: Pillars and Dimensions of AI Readiness
Global AI Readiness Ranking
In its ‘Government AI Readiness Index 2022’ report, Oxford Insights ranked 181 countries by how prepared their governments are to use AI in public services. The USA tops the rankings with a score of 85.72, followed by Singapore with an index of 84.12 and the United Kingdom in third with an index of 78.54. These countries scored far above the world’s average score of 44.61 out of 100. The global interest in AI comes amid a broader turn to digital government, further accelerated by the COVID-19 mitigation strategy that emphasized more on digital interaction.
However, best practices in AI strategies remain concentrated in countries in the global north, demonstrating a deepening divide in global AI readiness. Countries in the global south, particularly in Africa, need to catch up. The Sub-Saharan Africa region’s average score in the ‘2022 AI Readiness Index‘ was 29.38, the lowest globally, with many African countries at the lower end of the ranking spectrum. Regionally, Mauritius ranked first with an index score of 53.38, followed by South Africa (47.74) in second place and Kenya (40.36) in third place. The three countries within the Sub-Saharan African region with the least AI readiness index are Eritrea, the Central African Republic, and South Sudan, each having an index of 20.17, 19.90, and 19.45, respectively. Many African countries are still lagging as they have insufficient capacity and have not put in place the fundamental pillars necessary to embrace the world of AI.
Figure 2: AI Readiness Index in Sub-Saharan African Region
Challenges of AI Readiness in Africa
- Limited Digital Infrastructure: Many African countries lack the digital infrastructure to support AI development. For example, Internet penetration within Africa is low and was estimated at 28% in 2019. This sore state of internet penetration across Africa is due to infrastructure issues associated with the lack of access to electricity and low investment into internet infrastructure such as fibre-optic cables, cell towers, and base stations. The World Bank estimates that about 100 million Africans living in remote regions are inaccessible to mobile cellular networks and would require an investment of at least $100 billion to provide access to this marginalised group of individuals. Hence, digital infrastructural limitations in Africa affect the adoption and implementation of AI development within the continent.
- Lack of Quality Data: The need for more quality data is significant for AI development in Africa. African data ecosystems are still in the early stages of the African data revolution. Many African countries need more data collection mechanisms and more data governance frameworks, which result in better data quality. A few intricate algorithms are used to construct AI systems, and to train these algorithms, data is used. There is a data shortage in Africa and the majority of data collected does not accurately reflect the continent’s experience. This shortage raises the possibility that many algorithms may not be properly tailored to the characteristics of local inhabitants. Since AI can only function with data, a dearth of high-quality data is a drawback. In its absence, creating and implementing AI solutions would undoubtedly be harder.
- Ethical and Legal Considerations: There are widespread ethical and legal issues relating to AI in Africa. These issues revolve around the regulatory frameworks that impact the creation and application of AI technology required in Africa. Ethical and legal considerations relating to AI centre around safety and transparency, informed consent to use data, algorithmic fairness and biases, and data privacy. Hence, the need for clear regulations and guidelines for AI development in Africa creates uncertainties and limits innovation.
- High Cost and Skill Shortage: The cost of AI technologies, both hardware and software, is still high in many African countries, making it challenging to leverage AI technologies fully. More so, the shortage of skilled professionals in AI in Africa is because many countries in the region suffer from a shortage of AI specialists. For example, according to Rwanda’s Minister of State for Information and Communications Technology, there are only about 10 AI engineers in the country. Research also shows that thereneeds to be moretrained AI specialists in Ethiopia, and this is the case for many other African countries. In addition, African countries still need more education and training programs to develop the skills and expertise for AI development.
Looking Ahead: The Way Forward for African Countries
To address the challenges affecting AI readiness in Africa, it is crucial for countries within the region to leverage education to narrow the skill gap by adjusting educational curriculums to make them more technically oriented. Integrating AI training models at all levels of the education system would foster capacity building and talent development and encourage AI initiatives across sectors.
Moreover, investments in digital infrastructures like data centres, clouds, etc., would help develop a more AI-friendly digital economy. Additionally, public-private partnerships with tech giants and foreign start-ups accelerate infrastructural development.
Furthermore, supporting local tech companies through collaboration and investments will help empower organisations with the relevant skills and abilities needed to drive business innovations and AI strategies. In addition, lowering barriers to entry for tech companies would ensure that African countries have robust hubs of AI excellence.
It is also essential to emphasise the need for good data governance in improving public service delivery and output tailored to the needs of Africa’s transforming population. Consequently, creating a solid data collection mechanism to aid the acquisition of reliable data, establishing systems to recognise and prevent AI bias, and promoting fairness and transparency would help to change the current dynamics faced by many African countries.
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