Economic outcomes
By 2035, AI could double the GDP rate of African countries.4 According to Vineusa et al.’s assessment of the SDGs, AI could positively benefit 42 economic targets (70% of what the authors refer to as the “economic” SDG group) while negatively impacting 20 (33%).5 The economic benefits of AI and other emerging technologies often stem from their impact on productivity, both through improving within-sector productivity and through structural change.6 In three of the main sectors of the economy—agriculture, industry, and services— 4IR technologies can play a catalytic role in implementing public and private investment, regulation, and service delivery.7
However, inequality represents a major hurdle when it comes to the spread of emerging technologies, particularly, the challenge of ensuring that people are not left behind as jobs continue to change in the wake of new technology. Africa’s demographic future—by 2063 it will be home to half of the world’s total working-age population—makes this an extremely important challenge for the continent to overcome.8
Social outcomes
Based on Vinuesa et. al’s SDG assessment, AI has the potential to facilitate 67 targets (82%) within the “society” SDG group, including SDG 1 on no poverty, SDG 4 on quality education, SDG 6 on clean water and sanitation, SDG 7 on affordable and clean energy, and SDG 11 on sustainable cities.9 AI can act as an enabler to achieving these goals primarily through its potential to improve the production, provision, and distribution of food, health, water, and energy services while also contributing toward a circular economy that steers resources most efficiently to minimize waste.
Under SDG 3, which concerns good health and well-being, 4IR technologies are already helping African countries achieve health care outcomes and overcome health crises in more efficient and effective ways.10 For example, African AI startups are being deployed to monitor maternal health, produce diagnostic imaging, and develop AI-powered virtual clinic appointments.11
Within health care, Wang et. al are looking into how health care could benefit from a combination of smaller-scale, task-specific models and large-scale generic AIs which are currently less common in medicine.12 They find that large-scale AI models can play a particularly helpful role within medical dialog and medical image analysis; however, these models require a far larger amount of data, which could lead to their development being concentrated in regions outside of Africa.
Despite this potential, AI may also negatively impact 31 SDG targets (38% of Vinusa et. al’s “society” SDG group).13 These consequences stem from the inequality that may increase if these technologies are not evenly distributed (for example, if African small-scale farmers are left behind, meanwhile larger agricultural production companies in other regions capture the market with technology-based solutions), or if AI increases or reinforces discrimination against women and minorities through biased algorithms or image recognition.
Environmental outcomes
The relationship between AI/emerging technologies and the environment has been heavily researched, and the literature emphasizes contrasting impacts. There are important concerns to highlight regarding the environmental impact of AI and other advanced technologies, including resource depletion and carbon emissions if the high-energy needs of AI applications are met by non-carbon-neutral sources. On the other hand, AI has been identified as a tool that can help mitigate the effects of climate change through weather forecasting, early warning systems, land resource management, sustainable agriculture techniques, climate risk assessment, and more, as a GSMA study outlines.14
Vinusa et. al’s evaluation of the SDGs found that AI could be an enabler for 25 targets (93% of the environmental SDG group) due to its ability to better model the impacts of climate change, identify oil spills, identify desertification trends, and other use cases.15
Machine learning has also been found to be an effective tool for combating climate change by providing demand forecasts, optimizing electricity systems, accelerating clean energy technology development, and deploying smart grids and disaster management, according to Rolnick et. al.16 Combining machine learning with generative design and 3D printing can lead to a reduction in the need for carbon-intensive materials in construction and could even create a new “climate-friendly material.”17 Rane notes that generative AI could raise awareness of climate change, conservation, and adaptation for the public and professionals, including by providing assistance in tracking deforestation and wildlife, analyzing oceanic data, disseminating information about sustainable fishing practices, and more.18
According to Olatunde, Adelani, and Sikhakhane, AI, coupled with other emerging technologies such as cloud computing, the Internet of Things, and remote sensing, can help Africa optimize water management by reducing waste through leak detection systems, predicting equipment failures, and resolving contamination issues immediately, which is critical for environmental outcomes and has key spillovers to other development goals related to sanitation and agriculture.19
Governance outcomes
AI can significantly help African governments deliver public services and strengthen African governance. In Togo, for example, the government successfully used AI to refine targeting for the second phase of its Novissi cash transfer program, allowing 57,000 recipients of social funds in 100 of the poorest towns to be identified without contact.20
Rane finds that generative AI can help professionals and citizens navigate legal proceedings, increasing the efficiency of judicial and law enforcement while also increasing trust, transparency, and justice between citizens and institutions.21 Generative AI can also strengthen international cooperation and prevent cybercrimes by identifying potential threats. In Zambia, an AI-powered fact-checking tool called iVerify, which uses machine learning to detect hate speech and fact-check articles, was used during elections and garnered positive feedback.22
However, challenges arise within governance, as AI can also sow polarization and promote misinformation that can negatively impact social cohesion (thus negatively impacting SDG 10 on reduced inequalities) or could hinder access to justice if algorithms contain inherent biases.23
Gaps and challenges
A literature review on the 4IR in Africa by Kibe, Kwanya, and Nyagowa found that 4IR technologies are already being deployed for education, health services, e-commerce, tourism, records integrity, and project management in Africa.24 However, they find that African countries are held back from realizing the full potential of 4IR technologies for further development goals by a lack of 4IR skills, infrastructure, stakeholder involvement, and relevant policies. These gaps and challenges cluster around two issues: ethics and security and inequality.
Ethics and security
While it is clear that AI and emerging technologies have incredible potential for positive change, they can also be leveraged for nefarious purposes. According to the Institute of Development Studies, as of 2023, African governments were spending over $1 billion on digital surveillance technologies, some of which are used without the proper legal protections in place.25
There are also concerns about the human labor powering AI algorithms, which has so far been outsourced to non-Western countries, including in East Africa, where workers are paid a fraction of the wages they would receive elsewhere.26 In fact, in 2023, journalists described how OpenAI, the creator of ChatGPT, outsourced the human labor required to feed its algorithms to Kenyans who made less than $2 per hour.27 These reports make it clear that it will be critically important to reflect on the types of jobs AI will create or replace.
There are also ethical and security risks that arise from a lack of African data powering AI models. These risks are particularly amplified in the health care sector, where implementing models that provide diagnostic assistance or advice based on data from other countries can be impacted by local bias which can lead to troubling implications on health outcomes.28
Inequality in data, infrastructure, digital skills, and research and development
One of the main challenges facing African countries regarding AI and emerging technologies is the potential for increased inequality both within and between continents when it comes to data, infrastructure and connectivity, digital skills and human capital, and research and development.
Data
Because AI development relies on the availability of large amounts of data, there remain gaps and challenges in making sure that Africa is not left behind in terms of data production, quality, and accessibility. A variety of languages and local contexts are necessary to avoid bias, but trends in the proportions of local content on the internet are concerning. Only 0.02% of total internet content is in African languages (there is 2,650 times more English content), and as of 2023, only 2.5% of the global AI market comes from Africa.29
Infrastructure and connectivity
Many African countries still face a lack of electricity, internet, and broadband penetration, which ultimately constrains the deployment of advanced technologies.30 The African Development Bank estimates that infrastructure needs per year are between $130 billion and $170 billion, leading to a gap of $68 billion to $100 billion in financing.31 Sub-Saharan Africa has a 27% mobile internet connectivity rate, a 60% usage gap, and a 13% coverage gap, compared to the global averages of 57%, 39%, and 4%, respectively,32 meaning that many are within reach of connectivity but not able to use it.
Digital skills and human capital
The usage gap points to another key hurdle in the full implementation of AI for development: digital skills and human capital. Universities are starting to offer AI courses, but there is often a lack of opportunity for hands-on learning. The multidisciplinary skills needed to translate AI applications into development use cases may also be lacking, due to an exclusive focus on machine learning skills or data science.33
These skill gaps are particularly stark between African men and women. According to an ImpactHER survey in 2024, 86% of women surveyed across 52 African countries lack basic AI proficiency, 60% have not had digital skills training, 50.2% do not have (or have poor quality) internet access (37 percentage points lower than African men), and 34.7% do not own a digital device (with a stark contrast between urban and rural women: 15.7% of urban respondents reported no digital device ownership compared to 84.3% of rural respondents).34 The cost of AI training, information gaps in available programs, gender-based discrimination in access to digital skills training, and a lack of understanding of the benefits of digital skills were cited as challenges, alongside other factors such as cultural norms and time constraints.35
Research and development
Currently, AI funding, researchers, and publications are concentrated in the West, with the United States alone home to 60% of the top-tier AI researchers and $250 billion in private funding.36 Africa, South America, and most Asian countries have contributed less than 5% of peer-reviewed papers across AI subfields since 2014, while the U.S. and China have contributed 30% and 18% respectively.37 This leads to disparities in data and talent availability and potential biases in AI systems. Resource constraints and weak infrastructure hinder some African countries’ ability to rely on foundation models, let alone develop their own. Graphic processing units and cloud computing have high costs, which pose a challenge for researchers with limited resources.38 So far, AI startups in Africa have relied on grant funding, as the private sector has yet to overcome its risk aversion in investing in startups that use high tech innovation in science and engineering, in turn amplifying concerns that AI algorithms are being developed without African context.39 AI still represents only a small proportion of Africa’s $4 billion in total funding for tech startups in 2023.40
Strategies to unlock the potential of AI and emerging tech
Despite these challenges, some African countries have already emerged as leaders in AI research and development, AI startups, tech hubs, research initiatives, and government strategies for emerging technologies. Kenya in particular is leading the way in terms of public interest and engagement in AI. The 2024 Standard AI Index found that 27% of Kenyans use OpenAI daily.41 As African countries continue to face both the challenges and opportunities of AI, below are some key strategies to steer AI and emerging technologies toward maximum impact.
Implementing national, regional, and continental strategies
Seven African countries have developed AI strategies while others are creating specific entities to tackle these issues—Kenya’s Blockchain and AI Taskforce42 and South Africa’s Presidential Commission on the Fourth Industrial Revolution,43 for example. In July 2024, the African Union Executive Council endorsed its inaugural Continental AI Strategy, which calls for strengthening regional and global cooperation and a commitment to “an Africa-centric, development-focused approach to AI, promoting ethical, responsible, and equitable practices across the continent.”44 As continental and national strategies take shape, it will be important to ensure inclusive and collaborative processes that include perspectives on the ethics of AI. Key to moving these strategies forward will be the establishment of accountability mechanisms as well as clear ownership of the roles and responsibilities of each stakeholder.45
Supporting partnerships and support for AI research
Given the potential consequences of not being involved in the research and development of AI systems, Africa must prioritize partnerships between African universities, ministries, and private sector players with international players working in this space.46 Already, African universities are forming partnerships, including in Ghana, Uganda, and South Africa, where universities have formed AI labs focused on social impact. AI startups are also helping contribute to African-led research. Examples include Intron Health, a Nigerian startup that is developing a natural language processing tool for African accents in clinical settings and iCog labs, an Ethiopian startup that is developing an Amharic-speaking robot.47 As Asiegbu and Okolo point out, these innovations sprouted from local, grassroots communities who are training and spreading an AI research ecosystem.48 As AI becomes more advanced, African governments and universities must continue to make it a priority to pursue public and private research and development initiatives.49 More broadly, partnerships between the five key types of actors involved in AI (data holders, hardware/software providers, technical partners, domain experts, and financial partners) as identified by GSMA will be key for collaboration and synergy in order to implement positive AI use cases in Africa.50
Investing in data and AI research and development is a critical priority for African countries to help combat biases and reduce the potential for unequal development and deployment of these technologies and their benefits. Such investments include investing in local language data, in participatory approaches to data collection, and in access to existing data sources.51 Especially critical is the need for data that represents different demographic groups (particularly gender) in order to combat tendencies for AI algorithms to mirror and embed existing social inequalities, as well as robust data privacy and protection laws and clear guidance for data-sharing.52 Policies that promote publicly available datasets should also be prioritized so that local entrepreneurs can leverage AI to create local solutions.53
Developing 4IR-ready human capital
To maximize the widespread gains of AI and other emerging technologies, African countries need to close their skills gaps by investing in basic education and innovative approaches, including new financing models to be able to upgrade post-primary education and job training.56 Integrating emerging technologies into future-ready curricula can offer the rising youth population hands-on learning opportunities including through distance learning.57 Beyond reskilling, worker-centered programs should be explored to support productivity, workforce engagement, and holistic support, which will help shift mindsets from a fear-based focus on changing jobs and tasks to an opportunity mindset based on building capabilities and investing in people.58
Developing future-looking regulatory frameworks
Enabling anticipatory regulatory frameworks and making investments must be prioritized by policymakers to foster a thriving marketplace for emerging technologies,59 especially to address challenges such as inequality and ethics, and to purposefully steer technology toward uses for societal good.60 So far, use cases on the continent have been concentrated in IT services, computer software, and management consulting, with less focus on addressing development goals.61 Legal and regulatory frameworks along with incentives can help steer the focus toward local, inclusive, and sustainable AI solutions62 while also mitigating risks and biases.63 These frameworks should avoid blanket prohibitions, but rather should recognize that AI applications and use cases differ greatly from each other.64 As Davis, Signé, and Esposito explain, leaders must be equipped with three tools: transparent and holistic policymaking approaches, renewed efforts to collaborate across jurisdictions, and a shift toward agile governance.65
Investing in robust cybersecurity
Amid the rapid emergence and ever-evolving nature of these technologies, cybersecurity has become one of the most important investments.66 Cybersecurity should be addressed at multiple levels including the systems level, firm level, and individual level.67 Governments can be proactive in establishing cybersecurity agencies and adopting emergency response strategies,68 while private companies can promote cybersecurity skills for their employees and practice cyber risk protection within their leadership and decisionmaking.69 Awareness-raising and training will be key for citizens and for public, private, academic, and civil society organizations.70
Leveraging green technologies
Africa faces unique opportunities to exploit the positive reinforcing relationship between renewable energy and AI. Many African countries have high potential for renewable energy development, which they can leverage to improve access to electricity and, therefore, the deployment of data centers that can develop sustainable computer technologies.71 For now, AI use cases in energy in Africa are still in the nascent stage due to high initial investments, but with regulatory reforms that unlock private investment, African countries could become leaders in this area.72
Conclusion
Enthusiasm and innovation among African entrepreneurs and end-users are helping Africa forge ahead with the 4IR and its technologies, offering new pathways to development. However, with only five years left until the 2030 SDG deadline, African policymakers, development practitioners, and technologists alike are looking at the potential for AI and emerging technologies to help accelerate progress and unlock new innovations. The benefits of 4IR technologies for economic, social, environmental, and governance goals include wide reachability, lower costs, higher productivity, and new techniques to meet these goals, while their challenges include ethical concerns and perpetuated inequality. To navigate these realities, policymakers should focus on leveraging Africa’s strengths (entrepreneurship, youth population, etc.) when considering pathways forward. African countries need investments in factors that enable 4IR technologies to thrive, such as infrastructure and digital skills, as well as nuanced and forward-looking regulatory frameworks to encourage innovation while protecting citizens.
