Artificial Intelligence as a catalyst for poverty alleviation in Africa: challenges, opportunities, and pathways for inclusion

1 Introduction

Poverty in Africa is a multidimensional phenomenon that transcends mere income deprivation (Assis et al., 2024; Mhlanga, 2020). It encompasses deprivation in critical areas such as education, healthcare, nutrition, housing, and access to clean water and sanitation. The African Development Bank (AfDB) defines poverty as the inability to meet basic needs due to a lack of resources, infrastructure, and opportunities for socioeconomic advancement (Mhlanga, 2020). According to the World Bank, over 35% of Sub-Saharan Africa's population lives below the international poverty line of $2.15 per day, illustrating the severe economic disparities that persist across the region (World Bank, 2024). Integrating Artificial Intelligence (AI) into poverty alleviation strategies in Africa is both promising and challenging (Mpofu and Mhlanga, 2022). AI has transformative potential in addressing poverty through its ability to analyse large datasets, optimise resource allocation, and predict socioeconomic trends. For instance, AI-powered tools can improve agricultural productivity, enhance financial inclusion, and provide access to quality healthcare and education, even in remote areas (Mhlanga, 2024). However, these opportunities are accompanied by significant challenges. Limited access to digital infrastructure, insufficient investment in AI research, and a shortage of skilled professionals hinder the deployment of AI solutions.

Furthermore, ethical considerations surrounding the use of AI, such as algorithmic bias, data privacy concerns, and unequal access, pose additional barriers (Ndhlovu, 2025a). Despite these hurdles, the continent's young and dynamic population presents a unique opportunity to develop a robust AI ecosystem. Investments in capacity building, public-private partnerships, and international collaboration can pave the way for AI to catalyse inclusive development in Africa.

Despite global efforts to reduce poverty, Africa remains disproportionately affected, with millions of individuals lacking access to necessities such as income, education, healthcare, and essential services (World Bank, 2024). Though impactful in specific contexts, traditional poverty alleviation strategies often struggle to address systemic barriers such as resource constraints, corruption, and inadequate infrastructure (Chikandanga et al., 2025). These challenges persist in a rapidly digitalising world, where the potential of emerging technologies such as Artificial Intelligence (AI) to drive development has yet to be fully realised in Africa.

AI offers transformative opportunities for addressing poverty through data-driven insights, predictive analytics, and enhanced service delivery (Mhlanga, 2024). For instance, AI-powered tools can optimise agricultural productivity, expand financial inclusion, and deliver healthcare to underserved communities (UNESCO, 2022). However, the adoption of AI in poverty alleviation faces significant challenges. Limited digital infrastructure, a lack of skilled professionals, and ethical concerns such as algorithmic bias and data privacy threaten to deepen existing inequalities rather than alleviate them (UNESCO, 2022). Moreover, insufficient research and policy guidance are available to contextualise AI solutions for Africa's unique socioeconomic environment. This article seeks to address a pressing question: How can AI be effectively harnessed to alleviate poverty in Africa while ensuring equitable access, ethical application, and sustainable development? This article aims to provide policymakers, researchers, and practitioners with actionable insights by identifying the opportunities and challenges of AI integration. Ultimately, it seeks to contribute to the growing discourse on leveraging advanced technologies to achieve Sustainable Development Goal 1: Ending Poverty in All Its Forms Everywhere by 2030 (World Bank, 2024). This article explores the potential of AI as a tool for poverty alleviation in Africa. It begins by examining how AI applications can address specific dimensions of poverty, such as income disparity, food insecurity, and access to essential services. It also delves into the challenges hindering the adoption of AI solutions and offers policy recommendations to overcome them. By highlighting success stories and lessons learned, the article aims to provide a comprehensive understanding of AI's role in reducing poverty while ensuring its benefits are equitably distributed across all segments of society. Ultimately, the article seeks to contribute to the broader discourse on leveraging advanced technologies to achieve the United Nations’ Sustainable Development Goal 1: End Poverty in All Its Forms Everywhere by 2030.

2 Methodology

This article employs a qualitative approach, underpinned by a critical document analysis, to explore the transformative potential of Artificial Intelligence in poverty alleviation across Africa. It synthesises insights from secondary data sources, including peer-reviewed journals, reports from international organisations such as the World Bank, African Development Bank, and UNESCO, and case studies of AI-driven agriculture, healthcare, education, and financial inclusion initiatives. The article identifies the opportunities, challenges, and policy implications of integrating AI into poverty-reduction strategies through a thematic analysis. Data sources include Reports and publications from organisations such as the International Monetary Fund (IMF) and the Food and Agriculture Organisation (FAO) to highlight sectoral impacts. Academic articles discussing AI applications and ethical considerations in low-resource settings were also reviewed. Case studies of successful AI implementations in Africa, such as AI-powered microfinance and thoughtful city planning. The article evaluates AI's dual role in creating opportunities and exacerbating risks, emphasising the need for sustainable and inclusive solutions. It integrates multidisciplinary perspectives to provide actionable insights for policymakers, researchers, and practitioners.

3 Findings and discussion: AI applications for poverty alleviation

Artificial Intelligence (AI) offers transformative solutions to address poverty in Africa by enhancing productivity, increasing access to essential services, and optimising resource allocation across critical sectors. By leveraging advanced technologies, AI is revolutionising how poverty-related challenges are addressed, enabling targeted interventions and fostering sustainable development.

3.1 Agriculture: smart farming, precision agriculture, and food security

Agriculture remains the backbone of many African economies, employing over 60% of the population and contributing significantly to GDP (FAO, 2021). However, due to traditional farming methods, the sector faces numerous challenges, including climate change, mismanagement of resources, and low productivity (Ndhlovu, 2025b). AI-powered tools, such as precision agriculture and smart farming technologies, are transforming the agricultural landscape by providing data-driven insights to address these issues. Precision agriculture employs AI technologies such as machine learning, drones, and sensors to monitor critical variables, including soil health, moisture levels, crop growth, and pest infestations (Mgendi, 2024). For instance, AI-enabled drones provide real-time aerial imagery, helping farmers identify specific areas that require attention, such as replanting or additional irrigation. Similarly, soil sensors equipped with AI analyse nutrient levels and recommend precise fertiliser applications, reducing wastage and improving crop yields (Mhlanga, 2024). Weather prediction powered by AI is another crucial application, particularly in Africa, where unpredictable climatic conditions often disrupt farming activities. Machine learning algorithms analyse historical and real-time weather data to provide accurate forecasts, allowing farmers to plan planting and harvesting schedules effectively. This minimises losses due to unexpected weather events, such as droughts or floods, which are common across the continent (FAO, 2021).

Pest and disease management is a persistent challenge in African agriculture, resulting in significant annual losses. AI-powered systems detect early signs of infestations and diseases through image recognition. For example, AI applications can analyse images of crops to identify diseases such as maize streak virus or cassava mosaic disease, enabling timely interventions and preventing widespread crop damage. Beyond productivity, AI contributes to food security by reducing post-harvest losses, which are a significant issue in Africa (Mmbando, 2025). AI-powered storage solutions monitor temperature, humidity, and other factors in real-time to ensure optimal storage conditions for harvested crops. These systems minimise spoilage and help maintain food quality during transportation and storage.

Furthermore, AI technologies empower farmers by providing them with educational resources and decision-making tools. Mobile applications leveraging AI offer personalised advice on crop management, market prices, and weather conditions in local languages, making advanced agricultural knowledge accessible to smallholder farmers who form the majority of the agricultural workforce (UNESCO, 2022). The integration of AI in agriculture is not only enhancing productivity but also fostering sustainability and resilience. AI is playing a critical role in addressing food insecurity and poverty across Africa by optimising resource use, minimising losses, and empowering farmers with actionable insights (Nasaka and Ndhlovu, 2026).

3.2 Healthcare: disease prediction, telemedicine, and improved healthcare delivery in rural areas

AI has the potential to revolutionise healthcare delivery in Africa, addressing critical gaps in access, efficiency, and quality of care. These advancements are particularly impactful in rural areas, where a significant portion of the population resides and where medical professionals, facilities, and resources are often scarce. By leveraging AI, healthcare systems can provide innovative solutions to overcome these challenges and improve health outcomes across the continent. AI algorithms excel at analysing large, complex datasets to identify patterns and predict disease outbreaks (Thaldar and Bottomley, 2025). By processing epidemiological, environmental, and demographic data, AI systems can forecast potential outbreaks of diseases such as malaria, cholera, or Ebola with high accuracy. These predictions allow governments and public health organisations to implement timely and targeted interventions, such as vaccination campaigns, distribution of medical supplies, or public awareness programs (WHO, 2023). For example, AI-powered models have been used to predict malaria outbreaks by analysing weather conditions and mosquito breeding patterns, enabling early interventions in vulnerable regions.

AI-powered telemedicine platforms are transforming access to healthcare in rural and underserved communities. These platforms enable remote diagnosis and consultation, allowing patients to connect with healthcare providers through smartphones or other digital devices. AI-enhanced systems can analyse patient symptoms and medical histories to provide preliminary diagnoses, guiding healthcare providers in delivering precise, efficient care. This reduces the need for patients to travel long distances to urban healthcare centres, saving time and costs while ensuring timely medical attention (Ugar, 2026). Telemedicine applications also address the chronic shortage of healthcare professionals in rural areas by extending the reach of existing resources. For instance, AI-powered chatbots provide preliminary health advice and triage services, directing patients to appropriate care based on their symptoms. This improves access and alleviates the burden on overworked healthcare workers in these regions (UNESCO, 2022).

Diagnostics is one of the most impactful applications of AI in healthcare, particularly for diseases that disproportionately affect African populations, such as malaria, tuberculosis, and diabetes. AI-driven diagnostic tools analyse medical images, laboratory results, and patient records to detect diseases quickly and accurately. For example, AI algorithms can examine chest x-rays to identify tuberculosis cases or analyse blood samples for signs of malaria. These tools are especially valuable in resource-limited settings, where access to advanced diagnostic equipment and trained specialists is limited (Mhlanga, 2024). Furthermore, AI systems reduce diagnostic errors, enabling patients to receive appropriate treatment faster. Mobile diagnostic devices powered by AI are increasingly being deployed in rural areas, enabling healthcare workers to provide accurate diagnoses and treatments in remote settings.

3.3 Improving efficiency in public health

AI is revolutionising public health by enhancing the efficiency of healthcare systems, optimising resource allocation, and providing actionable insights to improve population health outcomes. These advancements go beyond individual patient care, addressing systemic challenges and strengthening healthcare delivery at scale. AI-powered systems optimise logistics, ensuring that medical supplies and resources reach the right locations at the right time. By analysing historical data, demand trends, and real-time conditions, AI can predict the need for essential medicines, vaccines, and medical equipment in different regions (WHO, 2023). This capability is especially critical in Africa, where uneven distribution of healthcare resources often leaves rural and underserved communities at a disadvantage. For instance, AI algorithms can identify areas at risk of supply shortages and direct stock accordingly, minimising disruptions and ensuring continuity of care. In addition to supply chain optimisation, AI is improving operational efficiency within healthcare facilities. By automating administrative tasks such as patient scheduling, record-keeping, and inventory management, AI reduces the burden on healthcare workers, allowing them to focus more on patient care. Hospitals and clinics equipped with AI tools can operate more efficiently, reducing waiting times and improving patient satisfaction (Mhlanga, 2024).

3.4 Monitoring and evaluating public health programs

AI-enabled tools are transforming the monitoring and evaluation of public health initiatives. By aggregating and analysing large datasets from multiple sources, such as electronic health records, disease surveillance systems, and community surveys, AI provides comprehensive insights into program performance. For example, AI can track vaccination coverage, identify gaps in service delivery, and evaluate the effectiveness of interventions, enabling policymakers to make informed decisions and improve outcomes (UNESCO, 2022). This data-driven approach ensures that resources are allocated where they are most needed and that health programs are continuously refined for maximum impact. AI technologies are also empowering healthcare workers and patients, fostering a more collaborative and informed healthcare ecosystem. AI-based decision-support systems provide medical practitioners with real-time insights and evidence-based recommendations, enhancing their diagnostic and treatment capabilities. For instance, AI tools can analyse a patient's medical history, symptoms, and diagnostic results to suggest potential diagnoses and treatment plans. These systems are particularly valuable in resource-limited settings, where healthcare workers may lack specialised training or access to advanced medical resources. By augmenting practitioners’ expertise, AI improves the quality and consistency of patient care (WHO, 2023).

Additionally, AI-powered training platforms enable healthcare workers to enhance their skills through virtual simulations and personalised learning modules. These platforms make advanced medical education accessible, even in remote areas, contributing to the continuous professional development of healthcare providers (Mhlanga, 2024). AI-enabled mobile applications are empowering patients by providing tools to proactively manage their health. These applications deliver personalised health education, medication adherence reminders, and follow-up appointment alerts, ensuring patients stay engaged in their care. In regions with limited healthcare access, AI-driven chatbots offer basic medical advice and triage services, helping patients understand their symptoms and directing them to appropriate care if needed (UNESCO, 2022). By fostering greater health literacy and engagement, AI empowers individuals to take charge of their well-being, reducing the prevalence of preventable diseases and improving health outcomes. AI's contributions to public health efficiency and patient empowerment have far-reaching implications for poverty alleviation. Improved healthcare delivery reduces the economic and social burdens of disease, enabling individuals and families to lead healthier and more productive lives. By addressing systemic inefficiencies and empowering both healthcare workers and patients, AI is transforming public health systems in Africa, contributing to sustainable development and reducing inequalities in access to care.

3.5 Education: personalised learning and access to digital education platforms

Education is a critical pathway out of poverty, and Artificial Intelligence (AI) is transforming the way education is delivered and experienced, particularly in Africa. By leveraging AI, learning experiences can be tailored to individual needs, thereby improving educational outcomes and fostering skills aligned with the demands of a digital economy. AI-driven educational platforms enable personalised learning by adapting content to match a student's pace, proficiency level, and learning style. For instance, machine learning algorithms analyse a student's progress and identify areas of strength and weakness, delivering customised lessons or exercises to address specific needs (WHO, 2023). This approach is particularly beneficial in Africa, where classrooms often face overcrowding, and teachers are stretched thin, making it difficult to provide individual attention. For students with learning disabilities or those who require additional support, AI-powered tools can offer targeted interventions, ensuring that no learner is left behind. These platforms also provide instant feedback, enabling students to correct mistakes and learn more effectively. Such personalised approaches enhance engagement, improve retention rates, and foster a deeper understanding of concepts. Access to quality education remains a significant challenge in Africa, especially in remote and underserved regions where schools may lack qualified teachers and adequate resources. AI-powered digital education platforms are bridging this gap by delivering high-quality learning materials to students regardless of their location. These platforms often feature multilingual, culturally relevant content, making education more accessible and inclusive (Chikandanga et al., 2025).

For example, AI-driven applications that operate on mobile devices enable students to access textbooks, video tutorials, and interactive quizzes, even in areas with limited internet connectivity. Offline functionality in some platforms ensures that learning continues uninterrupted, addressing disparities in access to technology and infrastructure. AI platforms are enhancing traditional education and preparing young Africans for the digital economy by offering skill-building programs. These platforms provide coding, data analysis, and digital marketing courses, equipping students with market-relevant skills. Additionally, AI-powered career guidance systems analyse individual aptitudes and market trends to recommend suitable career paths, helping students make informed decisions about their futures (Mhlanga, 2024).

By fostering digital literacy and technical skills, these initiatives empower young people to participate in and benefit from the growing digital economy, thereby reducing unemployment and poverty. AI technologies also enhance teachers’ capabilities, enabling them to provide more effective instruction. AI-powered tools help educators identify struggling students and tailor interventions to meet their needs. Virtual teaching assistants supported by AI can automate administrative tasks, such as grading assignments and tracking attendance, freeing up time for teachers to focus on lesson planning and student engagement (UNESCO, 2022). Moreover, AI-powered professional development platforms provide teachers with access to training and resources, improving their ability to deliver quality education. These tools ensure that teachers in even the most remote areas have opportunities to upskill and stay up to date with the latest teaching methodologies. By enhancing educational access, quality, and relevance, AI is transforming education into a powerful tool for poverty alleviation. Improved learning outcomes and skill acquisition enable individuals to secure better job opportunities, breaking the cycle of poverty. Furthermore, reducing educational disparities across regions fosters social equity and prepares the next generation of African leaders to navigate and contribute to a rapidly changing global economy.

3.6 Infrastructure development: smart city planning and resource optimisation

Artificial Intelligence (AI) is playing a pivotal role in transforming urban and peri-urban environments through smart city planning and resource optimisation. These innovations address critical challenges in resource-scarce settings, improving living conditions, fostering economic growth, and promoting sustainability. AI-powered systems are being used to manage traffic in densely populated cities, minimising congestion and reducing travel times. By analysing real-time data from traffic cameras, sensors, and GPS devices, AI can predict traffic patterns and adjust signals dynamically to optimise vehicle flow (UNESCO, 2022). This is particularly beneficial in African cities, where traffic congestion often results in economic losses due to delays and increased fuel consumption. AI-driven traffic management systems not only improve mobility but also reduce carbon emissions, promoting environmental sustainability. Waste management is a significant challenge in many urban areas across Africa, often leading to unsanitary conditions and environmental pollution. AI solutions are revolutionising waste management by analysing data on waste generation, collection routes, and recycling patterns. For instance, AI-powered platforms can optimise collection schedules to ensure timely disposal and reduce costs. Moreover, machine learning algorithms are used to sort and recycle waste more effectively, promoting circular economy principles and reducing reliance on landfills (Mhlanga, 2024). Energy distribution in urban areas is another critical area where AI significantly impacts. Smart grids powered by AI analyse energy consumption patterns and optimise electricity distribution, ensuring that supply meets demand efficiently.

These systems help reduce energy wastage and improve access to electricity, particularly in marginalised communities that often face power outages (UNESCO, 2022). Additionally, AI facilitates the integration of renewable energy sources, such as solar and wind, into urban energy grids, promoting sustainable energy solutions. Access to clean water and sanitation is a persistent issue in many African cities, especially in informal settlements. AI technologies are being employed to monitor water distribution systems and detect leaks or inefficiencies in real time. Predictive analytics powered by AI can forecast water demand and help allocate resources equitably, ensuring that marginalised populations have consistent access to clean water (WHO, 2023). These innovations directly improve the quality of life and health outcomes for urban residents. AI-driven infrastructure development improves living conditions and stimulates economic activities in urban and peri-urban areas. For instance, smart city technologies create new job opportunities in data analytics, technology maintenance, and urban planning sectors.

Furthermore, AI-enhanced urban planning enables more efficient land use and the development of sustainable housing projects, thereby addressing issues such as overcrowding and informal settlements (AfDB, 2020). By optimising resources and addressing systemic inefficiencies, AI helps create inclusive and resilient cities. These advancements align with the United Nations’ Sustainable Development Goal 11: Making Cities and Human Settlements Inclusive, Safe, Resilient, and Sustainable. In African contexts, where urbanisation is accelerating, AI-powered smart city initiatives offer a pathway to sustainable growth, reducing inequalities and enhancing overall quality of life.

3.7 Microfinance: enabling small-scale entrepreneurs through predictive credit scoring

Access to credit is a critical challenge for small-scale entrepreneurs in Africa, particularly for those operating in the informal sector without traditional financial records. Artificial Intelligence (AI) is addressing this issue through predictive credit scoring models that analyse alternative data sources to assess creditworthiness. These innovations empower entrepreneurs, drive economic growth, and reduce poverty by enabling microfinance institutions to provide loans to previously excluded individuals. Traditional credit scoring systems often rely on formal financial histories, which many small-scale entrepreneurs in Africa lack. AI-powered predictive credit scoring models overcome this barrier by analysing non-traditional data sources such as mobile money transactions, utility payment histories, social network activity, and even smartphone usage patterns (IMF, 2023). These models employ machine learning algorithms to identify patterns and assess repayment likelihood with remarkable accuracy. For example, a microfinance institution may use AI to evaluate an individual's spending habits, savings behaviour, and transaction frequency through mobile money platforms. By integrating such diverse data points, predictive credit scoring models provide a more comprehensive and inclusive assessment of creditworthiness, enabling lenders to extend financial services to underserved populations. Access to credit allows small-scale entrepreneurs to invest in their businesses, purchase equipment, expand operations, and hire additional employees. This creates a ripple effect, generating income and employment opportunities within local communities. For instance, a farmer using microfinance loans can invest in AI-powered agricultural tools, thereby enhancing productivity and increasing yields, benefiting the farmer and the local economy (Mhlanga, 2024).

AI-driven credit scoring also reduces the risk for microfinance institutions, as these models minimise default rates by accurately predicting repayment capacity. This increased confidence encourages financial institutions to extend loans to more borrowers, further expanding financial inclusion. AI-powered microfinance initiatives stimulate grassroots economic activity by providing small-scale entrepreneurs with access to credit. These efforts contribute to poverty alleviation by enabling individuals to generate sustainable income and build assets. Additionally, increased financial inclusion helps communities become more resilient to economic shocks, such as sudden price fluctuations or natural disasters. Women often face greater barriers to accessing formal financial services and benefit significantly from AI-driven microfinance. Predictive credit scoring models are gender-neutral, relying solely on data patterns rather than subjective biases, ensuring that women entrepreneurs have equitable access to credit. This inclusion empowers women to start and grow businesses, contributing to gender equity and broader social development (WHO, 2023).

Collaboration between microfinance institutions, technology providers, and governments can scale the impact of AI-powered credit scoring. Public-private partnerships can enhance the reach of these models, while regulatory frameworks ensure ethical use of data and fair lending practices. Such initiatives align with Sustainable Development Goals (SDG) 1 (No Poverty) and SDG 8 (Decent Work and Economic Growth), driving sustainable development across Africa. Despite its potential, the adoption of AI in microfinance faces challenges, including limited digital infrastructure, a lack of data standardisation, and concerns about data privacy. Addressing these barriers requires investments in digital ecosystems, capacity-building initiatives for microfinance institutions, and robust regulatory frameworks to protect borrowers’ rights. AI-powered predictive credit scoring transforms microfinance by bridging the gap between financial institutions and underserved populations. By enabling access to credit, these innovations empower small-scale entrepreneurs to achieve economic independence, fostering sustainable growth and reducing poverty at the grassroots level.

4 Impact of AI on poverty

The impact of Artificial Intelligence (AI) on poverty is multifaceted, encompassing both transformative solutions and significant challenges. AI offers unparalleled opportunities to address poverty by optimising resource allocation, improving service delivery, and enabling targeted interventions. However, its adoption also presents risks, such as deepening inequalities and creating unintended social and economic consequences, which require careful management. This section explores the positive contributions of AI to poverty alleviation, focusing on its applications in data collection, agriculture, and financial inclusion.

4.1 Positive impacts of AI on poverty alleviation

4.1.1 Data collection and analysis

One of AI's most transformative contributions to poverty alleviation is its ability to enhance data collection and analysis, enabling more precise and effective targeting of resources and interventions. Traditional methods of data collection often struggle with accuracy, timeliness, and scalability, particularly in low-resource settings. AI addresses these limitations by leveraging advanced analytics and geospatial technologies. AI-powered poverty maps utilise geospatial data, satellite imagery, and machine learning algorithms to provide a more precise and accurate representation of poverty distribution. These tools allow governments, NGOs, and international organisations to identify high-poverty areas, understand the underlying causes, and allocate resources more effectively (WHO, 2023). For example, AI can analyse nighttime light emissions, agricultural activity, and infrastructure density to infer socioeconomic conditions in remote or underserved regions. AI algorithms excel in analysing complex datasets that include socioeconomic indicators such as income, education, health, and housing. By integrating satellite imagery with ground survey data, AI systems can predict poverty levels with remarkable precision, even in areas where data collection has traditionally been challenging (Mhlanga, 2024). These predictive models highlight areas in need and provide insights into trends and potential future scenarios, enabling proactive rather than reactive interventions. AI enhances real-time data collection through tools such as mobile-based surveys and IoT-enabled devices. For instance, AI-powered systems can collect and analyse data from community health centres, schools, and financial services in real-time, providing policymakers with up-to-date information to inform poverty alleviation strategies. This capability is critical in dynamic contexts where socioeconomic conditions can change rapidly due to factors such as natural disasters, pandemics, or economic shocks. By providing high-quality data, AI empowers policymakers to design and implement evidence-based, locally tailored poverty-reduction programs. For example, AI insights can help optimise the distribution of social safety nets, identify regions requiring infrastructure development, and monitor the effectiveness of ongoing poverty alleviation initiatives. This ensures that limited resources are utilised efficiently and that interventions have a tangible impact on improving livelihoods (UNESCO, 2022). The integration of AI into data collection and analysis represents a paradigm shift in how poverty is understood and addressed. By providing accurate, real-time, and actionable insights, AI enables more effective targeting of resources and interventions, ultimately improving the lives of marginalised populations. As governments and organisations continue to adopt AI-driven solutions, it is essential to ensure that these technologies are used ethically and inclusively, bridging gaps rather than creating new inequalities.

4.1.2 Agriculture and financial inclusion

Artificial Intelligence (AI) is transforming agriculture and financial inclusion in Africa, addressing two critical sectors that underpin the continent's economic development and poverty alleviation efforts. By introducing innovative technologies and breaking down traditional barriers, AI is creating pathways for economic empowerment and resilience among marginalised communities. Agriculture employs over 60% of Africa's workforce and contributes significantly to the continent's GDP, yet it remains plagued by challenges such as low productivity, resource mismanagement, and vulnerability to climate change. AI is revolutionising this sector by enabling precision farming, optimising resource use, and improving decision-making processes. AI-powered technologies such as drones, sensors, and machine learning algorithms have introduced precision farming practices that enhance agricultural productivity. These tools enable farmers to monitor soil health, track crop growth, and detect pest infestations with unprecedented accuracy. For instance, drones equipped with AI analyse vast fields to identify areas requiring irrigation, fertilisation, or pest control, allowing farmers to allocate resources efficiently and reduce waste (FAO, 2021). This targeted approach increases yields and minimises environmental impact by reducing the overuse of inputs like water and chemicals. Climate variability poses significant challenges to African agriculture, where many farmers rely on rain-fed systems. AI-driven predictive analytics help farmers adapt to changing weather patterns by providing accurate forecasts and actionable insights. For example, machine learning models analyse historical and real-time weather data to predict rainfall, enabling farmers to plan planting and harvesting schedules effectively. By mitigating the risks associated with climate change, these tools help farmers reduce losses and ensure food security.

AI applications also play a critical role in empowering farmers with knowledge and skills. Mobile platforms powered by AI offer educational resources on modern farming techniques, pest management, and market trends. These platforms, often available in local languages, bridge knowledge gaps and encourage the adoption of advanced agricultural practices, fostering long-term sustainability and productivity improvements. In addition to transforming agriculture, AI is breaking down barriers to financial inclusion, a cornerstone of economic empowerment in Africa. Traditional banking systems often exclude individuals without formal credit histories or access to financial institutions, leaving millions unbanked. AI-powered solutions are bridging this gap by leveraging alternative data sources to assess creditworthiness and expand access to financial services. AI-driven credit scoring models analyse non-traditional data sources, such as mobile phone usage, utility payment records, and transaction histories, to evaluate an individual's creditworthiness. This innovation enables financial institutions to extend loans, savings accounts, and insurance to people previously excluded from formal banking systems (IMF, 2023). For example, a farmer without a traditional credit history can now access microloans based on mobile money transaction patterns, enabling investments in seeds, equipment, or livestock. By increasing access to credit and other financial services, AI fosters economic empowerment among marginalised populations. Entrepreneurs can expand their businesses, farmers can invest in productivity-enhancing technologies, and households can build resilience against economic shocks. This integration into the mainstream economy promotes financial independence and reduces poverty at the grassroots level. AI's ability to analyse vast datasets reduces the risk for financial institutions by improving the accuracy of credit assessments. This increased confidence encourages lenders to extend financial services to more customers, further expanding financial inclusion.

Additionally, AI-powered fraud detection systems safeguard transactions, enhancing trust in digital financial platforms. The integration of AI in agriculture and financial inclusion has far-reaching implications for poverty alleviation in Africa. AI enhances productivity, reduces losses, and improves food security in agriculture, directly addressing one of the continent's most pressing challenges. In finance, AI expands access to essential services, empowering individuals and communities to participate in and benefit from the economy. Together, these advancements build resilient livelihoods, foster inclusive growth, and reduce inequalities.

4.1.3 Energy poverty

Energy poverty, defined as the lack of access to modern energy services, remains a critical challenge for many African countries. AI is increasingly important in addressing this issue by driving technological progress and optimising energy systems. AI-powered solutions are being deployed to improve energy access, efficiency, and affordability. For example, AI algorithms can analyse vast amounts of data from energy grids to predict demand and optimise energy distribution to underserved areas (International Energy Agency [IEA], 2023). These systems are particularly beneficial in high-income and lower-middle-income countries where electrification programs are expanding but still face inefficiencies. AI-driven microgrid solutions that use machine learning to predict energy demand have been instrumental in providing reliable electricity to off-grid rural areas (UNESCO, 2022).

Furthermore, AI enables the integration of renewable energy sources, such as solar and wind, into existing energy systems. Smart grids powered by AI optimise the use of renewable energy, reducing costs and ensuring a consistent energy supply (Mhlanga, 2024). In Kenya, for example, AI systems are used to monitor solar power installations, ensuring maximum efficiency and reducing maintenance costs for communities relying on these energy sources (IEA, 2023). By enhancing human capital, AI also addresses the skills gap in the energy sector. AI-powered training programs provide individuals in underserved areas with the knowledge to maintain and expand energy systems, creating jobs and fostering community-led solutions. This dual impact of AI—enhancing both technological systems and human capabilities makes it a powerful tool for alleviating energy poverty.

4.1.4 Precision and populist AI

AI initiatives such as the World Bank's Development Impact Evaluation (DIME) and India's #AIforAll strategy demonstrate the potential of making AI solutions accessible and affordable to marginalised and remote communities. Precision AI refers to highly specialised AI applications tailored to address specific challenges. In the context of poverty alleviation, precision AI has been used to develop tools that predict poverty trends, optimise agricultural practices, and enhance public health interventions. For instance, DIME employs AI to evaluate the impact of poverty-reduction programs in real time, enabling policymakers to refine interventions dynamically based on accurate, actionable insights (WHO, 2023). Populist AI, on the other hand, focuses on democratising access to AI technologies to ensure inclusivity. India's #AIforAll initiative provides an excellent example of this approach, aiming to integrate AI into everyday applications that can benefit low-income and rural populations. This includes AI-powered chatbots providing access to government services in local languages and tools designed to improve digital literacy among underserved communities (UNESCO, 2022). By reducing the cost and complexity of AI solutions, such initiatives make them more accessible to those who need them the most. In Africa, similar approaches are emerging. For example, AI-powered mobile applications are being deployed to provide agricultural advice, financial education, and healthcare services to remote areas. These solutions bring essential services to underserved populations and empower individuals to participate in economic activities, fostering broader socioeconomic inclusion (Mhlanga, 2024). By combining precision AI's targeted effectiveness with populist AI's inclusivity, these strategies address both systemic and localised poverty challenges, ensuring that AI's benefits reach even the most marginalised communities.

4.1.5 Negative impacts and challenges of AI on poverty alleviation

While Artificial Intelligence (AI) offers transformative solutions to reduce poverty, it also presents significant risks and challenges. These negative impacts must be addressed to ensure that AI technologies contribute to inclusive and sustainable development. One of the most pronounced challenges associated with AI adoption is job displacement, particularly in sectors heavily reliant on unskilled or semi-skilled labour. AI technologies, including automation and robotics, are replacing repetitive, manual tasks across the manufacturing, agriculture, and retail industries. This shift reduces the demand for human labour, disproportionately impacting workers with limited skills and education (Gumede et al., 2026). In Africa, where a significant portion of the population depends on low-skilled jobs for their livelihoods, the adoption of AI could exacerbate income inequality and deepen poverty. For example, AI-powered mechanisation in agriculture may reduce employment opportunities for farm labourers, pushing them further into poverty if alternative economic activities are unavailable (Mhlanga, 2024). Moreover, the lack of social safety nets in many African countries amplifies the vulnerability of displaced workers, leaving them without support during transitions to new forms of employment. Efforts to mitigate these risks include investing in education and vocational training to equip workers with the skills needed in an AI-driven economy. Governments and private-sector stakeholders must also collaborate to develop policies that balance technological innovation with the protection of workers’ rights (UNESCO, 2022).

The other challenge is energy consumption. AI technologies, while promising to optimise energy usage, also contribute to increased energy consumption. Training and operating AI models requires significant computational power, thereby increasing energy demand. In contexts where energy resources are limited or reliant on low-grade, non-renewable sources, this increased consumption could exacerbate energy poverty (Mgendi, 2024). For instance, data centres powering AI systems consume vast amounts of electricity. In regions where coal or other polluting energy sources dominate, this can worsen environmental degradation and increase energy costs. In turn, households and small businesses that depend on these energy resources may face higher costs or reduced access, further marginalising low-income communities (WHO, 2023). This dual impact presents a paradox: while AI can optimise energy distribution and improve access in some cases, its energy-intensive nature can also strain existing systems. Mitigating this issue requires investments in renewable energy sources and the development of energy-efficient AI technologies. Countries must prioritise sustainable AI deployment to ensure that the benefits of AI are not outweighed by its environmental and social costs (Mhlanga, 2024).

The adoption of Artificial Intelligence (AI) has the potential to deepen global economic imbalances, creating disparities between developed and developing nations. While AI offers advanced tools for innovation and growth, its implementation can disrupt traditional economic models in developing countries, particularly those that rely on abundant, low-cost labour as a competitive advantage (UNESCO, 2022). In Africa, where labour-intensive industries such as agriculture and manufacturing drive economic activity, the automation of tasks through AI diminishes the relevance of this comparative advantage. As global companies adopt AI technologies to increase efficiency and reduce labour costs, the demand for African goods and services produced by low-cost labour may decline (WHO, 2023). This shift could lead to reduced export revenues, job losses, and widening income inequalities.

Furthermore, the disproportionate access to AI technologies exacerbates the divide between developed and developing nations. High-income countries dominate AI research and innovation, securing the majority of AI-related economic gains. In contrast, low-income nations often lack the infrastructure, funding, and expertise needed to implement AI solutions effectively (Mhlanga, 2024). This imbalance reinforces existing power dynamics in global trade and limits developing countries’ capacity to compete in an increasingly AI-driven global economy.

Additionally, the cost of acquiring and deploying AI technologies remains prohibitively high for many African governments and businesses. This financial barrier delays adoption and places these nations at a disadvantage in healthcare, education, and agriculture sectors, where AI could significantly improve outcomes (AfDB, 2020). To address these challenges, international cooperation and equitable policy frameworks are essential. Developed nations, multinational corporations, and global organisations should support technology transfer initiatives and capacity-building programs to enable developing countries to harness the potential of AI. Investments in Africa's digital infrastructure, AI literacy, and innovation ecosystems could help bridge the gap and create opportunities for inclusive growth (UNESCO, 2022). Moreover, policies promoting AI's ethical and equitable deployment must ensure that its benefits are shared globally rather than concentrated in a few advanced economies. Harnessing the potential of Artificial Intelligence (AI) for poverty alleviation requires proactive and equitable policy interventions. Governments, international organisations, and private stakeholders must collaborate to address the challenges posed by AI while maximising its benefits. Key policy considerations include government intervention, investment in AI research, and social equity measures.

4.1.6 Government intervention

Governments play a crucial role in shaping policies that enable societies to adapt to the changes AI brings. As automation and AI technologies transform labour markets, many workers, especially those in low-skilled and repetitive jobs, risk displacement (Gumede et al., 2026). To mitigate these impacts, governments must prioritise education and retraining programs that equip workers with the skills needed for the AI-driven economy. Vocational training and lifelong learning initiatives can help workers transition to new roles in technology-intensive industries, such as AI development, data analytics, and digital services (UNESCO, 2022). Additionally, governments should implement public policies that promote the integration of AI into traditional industries such as agriculture and manufacturing, ensuring these sectors remain competitive while minimising disruption. Effective labour market policies should also focus on protecting workers’ rights and fostering an environment where technology complements rather than replaces human labour. Public-private partnerships can facilitate such interventions, leveraging the resources and expertise of both sectors to create inclusive economic opportunities (WHO, 2023). Significant investments in research and development are essential for emerging economies to reap the benefits of AI. AI research tailored to local contexts can yield innovative solutions to address unique challenges such as food security, healthcare access, and financial inclusion (Mhlanga, 2024). Governments must allocate resources to establish AI research centres, encourage collaborations with international institutions, and incentivise private-sector investments in AI innovation.

Additionally, integrating AI research into national development strategies can align technological advancements with poverty reduction goals, particularly the United Nations’ Sustainable Development Goals (SDGs). For instance, AI-driven applications in precision agriculture and renewable energy can directly contribute to SDG 1 (No Poverty) and SDG 7 (Affordable and Clean Energy) (AfDB, 2020). Public funding for AI research should also emphasise ethical considerations and inclusivity, ensuring that AI technologies developed in these regions are accessible to marginalised populations. Furthermore, fostering a culture of innovation through educational initiatives, start-up incubators, and international exchange programs can strengthen local AI ecosystems and accelerate economic growth (UNESCO, 2022).

AI has the potential to exacerbate income inequality if its benefits are concentrated in specific groups or regions. To address this, governments must implement policies that promote social equity and ensure the fair distribution of AI's economic gains. Social protection programs, including universal basic income, unemployment insurance, and labour rights enforcement, can provide a safety net for workers affected by automation (Gumede et al., 2026). Income redistribution policies, such as progressive taxation, can help reduce the widening income gap caused by the unequal adoption of AI technologies (Mhlanga, 2024). Moreover, inclusive policymaking that involves marginalised communities in decision-making processes is essential. This ensures that AI-driven innovations are designed to meet the needs of the most vulnerable populations, reducing disparities in access to essential services such as healthcare, education, and financial services (WHO, 2023). Equity-focused policies must also address biases in AI systems that could further marginalise disadvantaged groups. Regulatory frameworks should enforce transparency and accountability in AI development, ensuring that algorithms are fair, unbiased, and inclusive (UNESCO, 2022).

5 Conclusion

Artificial Intelligence has immense potential to catalyse poverty alleviation in Africa, revolutionising agriculture, healthcare, education, infrastructure, and finance sectors. AI-powered technologies offer transformative solutions to improve productivity, enhance access to essential services, and empower marginalised populations. However, deploying AI must be approached with caution to address challenges such as job displacement, energy demands, and deepening economic inequalities. African governments, international organisations, and private stakeholders must collaborate to harness AI's potential effectively to build digital infrastructure, enhance AI literacy, and implement equitable policies. Investments in research and innovation tailored to Africa's unique socioeconomic context are crucial.

Furthermore, ethical considerations such as data privacy and algorithmic fairness must remain at the forefront to ensure that AI benefits all segments of society. This article underscores the importance of leveraging AI for sustainable and inclusive development, aligning with global efforts to achieve the Sustainable Development Goals, particularly Goal 1. With the right policies, partnerships, and investments, AI can become a powerful enabler of poverty alleviation and social transformation across Africa.

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Footnotes

Edited by: Peter David Tolmie, University of Siegen, Germany

Reviewed by: Rashesh Vaidya, Nepal Open University, Nepal

Md Wali Ullah, Westcliff University, United States