Young African man in a car holding a mobile phone and laughing
Microsoft Research Africa, Nairobi

AI and the Future of Work in Africa

A multi-disciplinary effort uniting experts across Africa to explore generative AI’s impact and strategize on creating a dignified work future for all.

AI and the Future of Work in Africa White Paper

Excerpt | Full report (PDF)

Executive summary

2024 AI and the Future of Work in Africa White Paper

This white paper is the output of a multidisciplinary workshop in Nairobi (November 2023), led by a cross-organisational team including Microsoft Research, Microsoft Philanthropies, University of Pretoria, NEPAD, Lelapa AI, and Oxford University. The workshop brought together diverse thought-leaders from various sectors and backgrounds to discuss the implications of generative AI for the future of work in Africa. Discussions centered around four key themes: Macroeconomic impacts; Jobs, skills and labour markets; Workers’ perspectives, and Africa-centric AI platforms. The white paper provides an overview of the current state and trends of generative AI and its applications in different domains, as well as the challenges and risks associated with its adoption and regulation. It represents a diverse set of perspectives to create a set of insights and recommendations which aim to encourage debate and collaborative action towards creating a dignified future of work for everyone across Africa.


The introduction outlines the demographic and socio-economic context in Africa, as it pertains to work. This includes a young population, its often-rural nature and the rich mix of diverse ethnic groups, cultures, religions, and languages. Given this, the African context presents both unique opportunities and challenges, when we consider the potential of generative AI to positively transform work. This is compounded by the fact that the performance of generative AI models depends on the amount and quality of training data, yet the majority of the training data for existing generative AI models is sourced from the predominantly English-speaking Global North and as such does not well represent African social and cultural realities.

Collage image showcasing different workers, sectors, and technologies

Macroeconomic impacts

The impact of AI on the future that emerges will be a consequence of many things, including technological and policy decisions made today. Getting to a better future will require carefully designed policies and regulations that foster the development of AI while keeping the negative effects in check. This section discusses the potential impacts of generative AI on three broad areas of macroeconomic interest: productivity growth, labour markets and income inequality, and industrial concentration. It outlines how if leaders wish to maximize the benefits and mitigate the macroeconomic risks related to generative AI, they must invest in digital infrastructure and human capital – including education initiatives – whilst ensuring that AI development is inclusive and tailored to the continent’s unique needs and challenges. Addressing these issues is essential to ensure that AI acts as a catalyst for equitable and sustainable growth in Africa.

Jobs, skills, and labour markets

Africa’s young population and vibrant tech ecosystem provide significant opportunities to position Africa as a leader in technological innovation and sustainable development. The section explores the different potential outcomes on labour markets of the deployment of generative AI – from the potential to enable African youth to forge ahead to potential labour market disruption potentially increasing income inequality. It highlights the need for research which moves beyond the usual generalizations to apply a critical lens to understand the nuances of the repercussions of generative AI in Africa’s unique social and economic contexts. It highlights the importance of:

  • Preparedness: Governments, educational bodies, and employers must be agile in reskilling workers. Overarching effort is needed to ensure these transformations improve the quality of the work produced and support and enhance the creativity and value of workers, rather than using AI to automate work – as this will inevitably result in a race to the bottom.
  • Local AI Leadership: For Africa to significantly contribute to the AI economy, it is essential to cultivate African talent in AI research, innovation, and design, as well as policy and governance. This of course requires building and consolidating expertise in computer science, machine learning, natural language processing and engineering – the technical skills typically associated with AI development. However, it is clear that such skills are not enough on their own if we are to build AI which enhances human work and creativity. Rather it is important to create environments where multi-disciplinarity can flourish – including the social sciences, ethics, human computer interaction, law and policy – and ensure that diverse perspectives from across society are involved.
  • Skill Development: People need the skills, knowledge, and access to leverage generative AI in their work and careers. Given the tools propensity for fabrication, knowing how to evaluate and appropriately deploy their output will become an important new business skill. Additionally, it is important that the human work of building and maintaining AI systems is recognised and valued as skilled labour.

Workers’ perspectives

African workers are highly diverse, from urban to rural, frontline to information workers, start-ups to enterprises, and with up to 85% working in the informal sector. The impacts of generative AI are not likely to be equally distributed across workforces. This section explores what an ideal future might look like for African workers working with generative AI – including centering African perspectives, work and wellbeing and social contributions. It then goes on to examine the cultural and social alignment and clashes between generative AI and African perspectives, with a focus on language, context, culture, and data.

  • Language. Whilst African languages are increasingly represented in large language models (LLMs), they lag behind English performance substantially and currently only a small number are well represented. However, LLMs do perform much better with code-mixed and naturally produced language than previous language technologies, opening up the possibility of better tools in domains such as healthcare and agriculture. However, speech models currently lag behind.
  • Culture and context. African culture and context are also notably underrepresented in generative AI training data, leading to poor performance in African workplaces. Representative African data is key to building models which work in African contexts, and this means creating equitable data ecosystems, and incorporating indigenous knowledge in culturally and socially sensitive ways.
  • Data. The importance of data justice and data sovereignty is highlighted as central to Globally Equitable Generative AI.

Recommendations include centering a communal focus over individualism to balance the needs of communities and individuals; Supporting the substantial informal sector, emphasizing empowerment, entrepreneurship, and job creation over efficiency; Bridging the digital divide, with infrastructure improvements but also edge computing and lower resource AI; Prioritizing sustainable development and well-being; and finding ways to respect and integrate Africa’s rich traditional knowledge.

Collage image surrounding the map of Africa highlighting the diversity of people, technology and talent

Africa-centric AI platforms

Africa-centric AI refers to the design, development, validation and deployment of AI solutions with a strong focus on African context. The emergence of Africa-centric AI tools and platforms addresses unique socio-economic challenges by tailoring AI solutions to the continent’s specific needs. This section discusses a potential dystopian future – where generative AI exacerbates existing social inequalities – and a potential utopian one – where AI acts as an equalizer. It discusses how the actions taken today determine the future trajectory and how the choice of which world we steer towards is a collective responsibility, requiring engagement from policymakers, technologists, and citizens alike. Ensuring a beneficial outcome with generative AI involves proactive governance, inclusive design, investment in education, and a commitment to regulatory and ethical standards. Recommendations include 1) a commitment to ethical development, transparency, and bias mitigation, 2) Robust regulatory oversight to balance innovation with safeguards against misuse, 3) Encouraging entrepreneurship and innovation, 4) Expanding the grassroots AI communities in Africa, and 5) Learning from communities outside of Africa.


Generative AI presents a powerful tool for shaping a dignified future of work in Africa. By proactively addressing the challenges and harnessing the opportunities, Africa can leverage AI to drive economic growth, empower its workforce, and become a leader in socially responsible AI development.

Overall, the recommendations for a Dignified Future of Work for all with Generative AI include

  • Invest in infrastructure and education: Africa needs strong infrastructure and a skilled workforce to maximize the benefits of AI. 
  • Develop inclusive AI policies: National and regional AI policies focused on inclusive education, worker protection, and stakeholder involvement are essential.
  • Focus on human-centered design: AI should complement human skills, not replace them. Training data and AI tools should be developed with African contexts in mind.
  • Prioritize African-centric solutions: Africa-centric AI platforms designed with local expertise can address the continent’s specific challenges. Collaboration among stakeholders is key for responsible AI development that respects local knowledge and traditions.

As evidenced in the workshop, the involvement of youth, community leaders, academics, and business leaders are critical in developing inclusive and relevant AI policies for Africa. This requires a more agile consultative policy formulation process with sufficient scope for improvement as the Generative AI space evolves. Furthermore, the need for wide disciplinary involvement in the design and building of Generative AI models, platforms and applications is central.