Economic Development in Africa through AI
Artificial intelligence (AI) has the potential to transform Africa’s economy by generating employment opportunities and fueling growth across diverse sectors (Ambali et al., 2023; Jaldi, 2023). African nations can leverage AI to enhance productivity, allocate resources efficiently, and strengthen global competitiveness.
In the manufacturing sector, AI’s capacity to automate production, improve quality control, and reduce costs enhances the competitiveness of African manufacturers globally (Ngila, 2022; Cindy, 2023). The AI industry is still in its early stages in Africa, but it is growing rapidly, with over 2,400 specialized companies, 40% of which were established in the last five years (Ngila, 2022).
AI is also having a significant impact on the services sector, propelling customer service enhancements, personalized marketing campaigns, and streamlined supply chains. These improvements are leading to heightened customer satisfaction and loyalty, which subsequently augment sales and revenue (Zia, 2023; Leong, 2023). For example, AI-powered chatbots are now being used by many African businesses to offer round-the-clock customer support, freeing up human representatives to focus on more complex issues.
AI is also serving as a catalyst for innovative product and service creation, which is engendering employment opportunities and fostering economic prosperity (Manchidi, 2023). For example, AI-driven educational platforms are providing high-quality learning experiences to students across Africa, nurturing a more skilled workforce and contributing to elevated economic output.
There are already a number of real-world instances that highlight AI’s current role in cultivating employment opportunities and driving economic progress across Africa. For example:
- In Kenya, AI-powered chatbots have transformed customer service for banks and telecom companies, optimizing the focus of human customer service representatives on complex matters while concurrently enhancing customer satisfaction.
- In South Africa, AI innovations in agriculture have led to higher yields and reduced losses, bolstering food security and generating jobs within the agricultural sector.
- In Nigeria, AI-driven educational platforms provide high-quality learning experiences to students nationwide, thereby enhancing workforce proficiency and economic dynamism.
These examples provide insights into AI’s ongoing transformation of Africa’s economic landscape. With the continued advancement of AI, a vista of innovation and job creation looms on the horizon. Africa is uniquely positioned to harness AI’s potential, given its burgeoning middle class, youthful and expanding population, and abundant natural resources (Kuyoro & Leke, 2023). Through the adoption of prudent policies and targeted investments, Africa is primed to assume a leading global role in AI.
Challenges to AI Adoption in Africa
African data and infrastructure
Lack of access to high-quality data is a fundamental impediment to the development and deployment of artificial intelligence (AI) in Africa. AI systems rely on data for training, and the quality of the data directly impacts their performance. One of the main challenges is the scarcity of African-origin datasets that are relevant, reliable, and of high quality to support research, development, and innovation (Alami et al, 2020). This is due to a number of factors, including limited data infrastructure, such as reliable internet access, electricity, and secure data storage facilities; limited knowledge and understanding of the importance of data quality among many organizations in Africa and cultural barriers to collecting and using data in Africa. Despite the limited data infrastructure in Africa, some countries, such as South Africa, Tunisia, Morocco, Kenya, Seychelles, Botswana, and Nigeria, have scored highest in the “data & infrastructure” pillar of the Government AI Readiness Index 2022. However, broadband coverage in Africa remains low, with the majority of people lacking access to mobile internet. This lack of connectivity is a barrier to AI adoption in the region.
Africa’s lack of structured data ecosystems can hinder the development and deployment of AI-powered solutions, widening the digital divide between the global north and south (Ade-Ibijola & Okonkwo, 2023). The lack of access to high-quality data has a number of negative consequences for AI development in Africa. It prevents AI developers from training accurate and reliable AI systems, makes it difficult for African AI developers to compete with their counterparts in developed countries, and slows down the adoption of AI in Africa.
Africa is collecting less data than it should, and much of the data that is collected is incomplete or gathered in the global North. This gives machines a limited understanding of the African context, which makes it impossible for Africa to compete with the global north in terms of AI. Therefore, there is a need for all African countries to embrace the African Union (AU) Data Policy Framework, which was endorsed by the AU Executive Council in February 2022. This framework is a blueprint for building a unified data ecosystem in Africa, where data can flow freely and securely across the continent, while safeguarding human rights, upholding security, and ensuring that everyone benefits.
Skilled AI Workforce in Africa
Africa’s shortage of skilled workers is a major barrier to AI adoption. This is due in part to the fact that the curricula of many African schools have not been updated to reflect the needs of the AI era. As a result, students are not learning the skills they need to develop and deploy AI solutions. Even when they do learn about AI, they may not be getting the kind of education they need to succeed in the AI workforce. Additionally, there are not many AI programs offered at African universities, and those that are offered can be expensive. This creates a shortage of AI-skilled workers in Africa.
A 2021 UNESCO survey of eight African countries found that universities are in the process of developing AI courses, and there is interest in incorporating AI education at the secondary school level. However, in 12 countries, no specific measures for AI skills and education have been implemented at university or school level, but there is an interest to do so. In four countries, the level of incorporation of AI in research and education varies widely across universities and educational institutions (Bhanu & Prateek, 2021). This suggests that while there is a growing recognition of the importance of AI skills and education in Africa, there is still a long way to go in terms of implementation. The shortage of AI skills in Africa is a challenge, but it is also an opportunity. By investing in AI skills and education, Africa can position itself to become a leader in the global AI economy.
The African brain drain is a major obstacle to AI adoption on the continent (AUDA-NEPAD 2021, Macaulay 2022, Idoko 2023). Every year, thousands of skilled Africans leave for developed countries in search of better opportunities (Kohnert, 2007, Cha’ngom, 2020, Obadare, 2023). Many of these workers are experts in AI, and their departure leaves Africa with a shortage of critical talent. This shortage is having a ripple effect throughout the continent. It is slowing down the development of AI solutions that could address Africa’s most pressing challenges, from improving healthcare to boosting agricultural productivity. The Japa Syndrome, the exodus of skilled workers, is exacerbating this problem (Adaramola, 2023, Okunade & Awosusi, 2023). More and more Africans are choosing to leave their home countries in search of better opportunities. This is leaving Africa with a shrinking pool of talent, and it is making it even more difficult to develop and adopt AI solutions.
Equitable Funding and Resources
As global interest in and use of AI continues to grow, so does the importance of equitable, substantial, and specific funding support for AI research and development (R&D) in Africa. Without this support, the benefits of AI will only be accessible to a select few, exacerbating existing inequalities.
A new report by AI Expo Africa, (2022) has found that AI is a cross-cutting technology that is impacting at least 120 market segments across Africa. Privately owned small and medium-sized enterprises (SMMEs) and micro businesses make up 75% of the AI sector in Africa. There is still a funding gap for AI adoption in Africa, due to the lack of early-stage investors. According to a survey by Partech Africa, 80% of African startups struggle to access early-stage funding (Partech Africa, 2023). This is in stark contrast to more developed AI ecosystems, where there is a thriving early-stage investment landscape.
Multilingual and Cultural Adaptation
- Multilingual adaptation: AI systems must be trained on large-scale multilingual datasets to cater to the vast array of languages spoken worldwide (Lewis et al., 2021). Recent advancements in NLP have allowed researchers to develop techniques for training AI models that can understand and generate text in multiple languages (Wu et al., 2019).
- Cultural adaptation: AI systems must be tailored to suit the needs of various user groups by understanding the cultural norms, values, and preferences of different cultures (Fiesler et al., 2020). For example, an AI-powered virtual assistant may need to adjust its behavior, tone, and responses based on cultural expectations and sensitivities.
- Principles for effective multilingual and cultural adaptation: The development of AI systems can be guided by principles such as inclusive design and user-centered approaches (Abascal et al., 2017). Inclusive design emphasizes the importance of considering the needs and perspectives of diverse user groups from the outset of the design process. Collaborations between AI researchers, linguists, sociologists, and anthropologists can contribute to a more comprehensive understanding of the linguistic and cultural dimensions of AI systems (Benjamin, 2019). Another key aspect of developing multilingual and culturally adaptive AI systems is the availability of high-quality language resources (Zampieri et al., 2020).
Developing AI systems that can effectively address linguistic and cultural diversity, making AI accessible and relevant to populations with different languages and backgrounds.
