1Africa is the youngest continent in the world;1 its working age population is projected to grow by 70 percent (or 450 million people) by 2035.2 Meanwhile, the rapid developments and diffusion of AI are expected to transform the nature of work. The outsize impacts of AI on jobs are expected to be in the formal sector, but in Africa, 8 out of 10 workers are in informal employment;3 they are engaged in economic activities that are not regulated or taxed by government, and that offer limited social security benefits. Furthermore, as 10-12 million youth enter the labor force in Africa every year, there are only about 3 million new formal sector jobs created.4 If and how AI will affect the future of work for Africa’s growing workforce is an important policy question against this backdrop.
Figure 1: Percent of informal enterprises in relation to total enterprises, by sex of the owner (%)
Source: Adapted from ILO (2023).5
2Skills and capacity questions come up often in most discourse about development in Africa. They tend to be framed around lack and need, leading to calls for and a flurry of skilling and capacity building initiatives. This may stem from the longstanding challenges on the continent in offering affordable and quality formal education ‒ over 100 million children are excluded from the formal education system in Africa.6 Informal education, with its roots in indigenous African societies, remains a crucial lever for imparting knowledge, offering an important, and arguably underappreciated alternative to formal learning. The continent’s largest employment sector is also informal, and it is where even educated workforces are absorbed, owing to insufficient job opportunities in the formal sector. The harsh and complex realities around education on the continent have perhaps seeded, among some people, the notion that Africans are “under-skilled” ‒ even though ingenuity, innovation and resilience are manifest, especially in the informalized economies within which a majority operate. The dichotomy of “high” and “low” skills, where the former are associated with tertiary education and the latter with manual labor are indicative of long held yet increasingly criticized beliefs and narratives which have serious policy implications.7
3“Digital skills trainings” are often recommended and pursued in a quest to impart tech literacy, to drive consumption, use, and production or innovation using digital technologies. Efforts here typically focus on individuals and groups in society such as women, youth and marginalized communities, and tailored to different educational statuses. Capacity building, meanwhile, typically used to refer to government and other formal institutions’ capabilities to adopt and use digital technologies; and in the case of governments and respective agencies, to develop or reform policies, laws and regulations affecting the overall ecosystem. Meanwhile, entrepreneurship is touted as a panacea of sorts in job creation and economic growth in Africa, especially in a digital era. Micro, small and medium-size enterprises (MSMEs) account for up to 90 percent of African businesses and are a major source of employment.8 Young people and women in particular are encouraged to become entrepreneurs, typically in the MSME space, as an alternative to seeking (formal) jobs. Digital technologies are now widely considered to be viable avenues for enterprise creation as they purportedly lower entry barriers and create new market niches. Digital skills initiatives targeting women and youth typically set out to enhance their ability to build and scale innovations that are “bankable”, and that respond to social and environmental issues. Social entrepreneurship has gained prominence as a policy proposal for creating jobs and advancing inclusivity within Africa and of Africa in the global economy9 by combining the best of digital technologies and sustainable development goals on the continent.10
4Skills development is widely considered a key fundamental pillar for realizing the widely touted potential of Africa’s digital economy. To realize this, calls for skilling, upskilling and reskilling Africans are growing louder in public, private, philanthropy and development sector circles.11
5Additionally, it is recommended that “AI capacity” at national, regional and continental level be fostered through skills and educational lifelong learning frameworks to attain a skilled AI workforce, in combination with expanding equitable access to digital and AI infrastructure, tools and resources, as well as the enforcement of comprehensive policies and regulations that support human-centered AI development and use.12 This is reflected, for instance, in the AI Talent Readiness Index developed by Qhala, a pan African digital consultancy. The index gauges African countries’ “capacity to develop, retrain and deploy AI talent”, and measures this across digital skills, data and infrastructure, and government readiness pillars.
Figure 2: Top 10 overall country performance rankings in the Qhala AI Talent Readiness Index for Africa
Source: Adapted from Qhala and Qubit Hub (2025).13
6“AI skills” do not have a ready, universal definition, but different actors have outlined levels and indicators to assess them. In its AI sprinters report,14 Google outlines three competency (“AI fluency”) levels for an AI-ready workforce, that is: AI Learners who are equipped with basic AI literacy, AI Implementers who can leverage and adapt AI tools at work, and AI Innovators with deep technical expertise who can contribute to shaping how the technology evolves. Qhala’s AI Talent Readiness Index, meanwhile, offers a more comprehensive set of indicators for evaluating digital skills in a country context. The indicators are adult literacy rate; labor force with advanced (tertiary) education; Information, Communication and Technology (ICT) skills/digital competencies in the education system (from primary to tertiary level); share of female graduates in STEM (Science, Technology, Engineering, Mathematics); percentage of the workforce in the gig economy (digital platform work); number of professional software developers per million population, and number of institutions of higher learning teaching AI/Machine Learning.15
7As AI’s diffusion— especially with generative AI—has gained traction, calls for “AI skills” are growing louder. In some quarters, they are considered part of a continuum in availing STEM education, bolstering research and development (R&D) capacity, and building digital skills.16 In others, AI skilling demands distinct shifts. Coding, for instance, is promoted as part of the “basic literacy for the digital age”, and numerous digital skills initiatives on the continent over the past two decades have placed emphasis on this. However, it is now posited that with the shifts from generative to agentic AI, we need more nuanced “AI readiness skills” demanding systemic change, over mere individual skills development.17
8Up to half of the new entrants into the global workforce by 2030 will be African, necessitating the creation of 15 million new jobs annually.18 Endless skilling will not per se solve for the lack of job opportunities to apply these skills. And the quality of these jobs matters just as much as the quantity. If AI is poised to disrupt what constitute formal sector jobs, it is imperative on African stakeholders to critically examine what this portends not only where jobs will be created, but also the (dis)incentives that are in place for competitive enterprises to grow sustainably and generate employment opportunities. Sectors like agriculture which absorb a significant number of the existing workforce on the continent, can be reorganized so that they are more productive and leverage AI tools where appropriate. But for that to happen, the structural and systemic constraints to growing and sustaining vibrant agricultural sectors, including improving value chains, must be addressed. Africa must think in “both-and”, not “either-or” where traditional sectors and emerging technologies like AI intersect. While AI skills and tools are important, so too are the roads, electricity, ubiquitous internet connectivity, and other enabling infrastructures as well as policy, regulatory and legislative reform to enable entrepreneurship in both formal and informal sectors to thrive.
9It is therefore very important to (re)frame the skills question — from analogue to AI — within the lens of Africa’s sociopolitical, economic and cultural realities and complexities. While indeed there may be a shortage of “AI skills” for instance, it is not an entirely appropriate brush with which to paint the entire landscape of skills and talent on the continent. There are diverse forms of skills, such as rich cultural and contextual knowledge, which are also relevant to shaping relevant applications of even the most advanced AI tools, and that are not (yet) part of the general corpora training current AI models. It is also important to recalibrate our assumptions around skilling: starting with whether it is jobs that create skills, or skills that create jobs.
10Over eighty percent of employment in Africa is informal, the highest rate globally.19 The informal sector is a major source of income and employment for what are considered “low-skilled” individuals, but it is also where many of the continent’s young employment seekers— including those well-educated— are absorbed, given the limited opportunities for readily available formal sector jobs. Women are “disproportionately overrepresented” in Africa’s informal economies,20 owing in part to adverse gender norms and gender-biased laws that lead to lower levels of education and opportunities available to them in the formal sector.
11As has been rightfully noted by international organizations recently, the policy narratives around informal work in Africa are due for a paradigm shift. Rather than dismissing and perceiving this kind of work as a threat demanding control or elimination, instead, the focus ought to be in how the sector can be better supported and invested in to catalyse economic transformation at the structural level and in an inclusive manner.21 This is important for considering the impacts of AI on the present and future of work on a continent whose main form of employment falls outside of the main purview for which assumptions, projections and narratives around digital and emerging technologies are formulated and deployed. It has implications for how skills (and lack thereof) are defined, what AI can meaningfully offer to this vast and dynamic sector, and most importantly, who is included or excluded in shaping discussions on areas of intervention. It also matters for how digital skills in Africa are measured, and the skills already employed in service to the AI industry are factored in. For example, thousands of young Africans working under harsh labor conditions have already contributed to the success of generative AI models22— despite being widely unacknowledged by the big tech players and being subjected to precarious work excused as attempts not to distort the labor market.23
12One argument that has been put forth is that, rather than full-time formal sector jobs, the future of work in Africa will be in “somewhat formal” entities and people working multiple gigs.24 This is already the case, thanks in part to advancing digitalization. Employers are expected to focus on specialisms, rather than (full-time) employees, which could expand access to work for women, youth, disabled and marginalized groups as gigs offer flexibility in working hours or in working remotely.25 Therefore, the role of digital platforms and emerging technologies like AI would ideally be to support decent and more productive work, whether formal or informal. Generative AI tools, for example, can support increased and customizable business tools such as predictive analytics, accounting, automated customer service and communications which can progressively support formalization of enterprises, and creating room for the business entities to concentrate on core offerings and unique value propositions. The skills needed to utilize such tools might fall under the “basic AI literacy” bucket, with the additional advantage of the ease of customization to leverage voice and be used in multiple languages.
13It is also important to bear in mind that while the informal sector is considered a “low productivity sector”, its dynamism is in part due to the fact that it creates and sustains jobs that may not be easily automated. Even for those that can technically be automated, the informal sector sustains the relationality — notably, trust— that is a core feature of African social relations, that machines cannot readily mediate or even substitute. Furthermore, there are unique forms of knowledge and know-how that exist in the informal nature of work in these domains, that may not yet be readily available to the training models for AI systems that would replace such work and workers. In such a paradigm, exploring if and how AI can enhance productivity in such lines of work, rather than in replacing them altogether will be a critical determinant of success for any digital or AI-driven interventions. The past few years’ aggressive efforts to digitalize certain sectors and value chains on the continent have certainly showcased the resilience of the informal sector and the need to better understand it, before attempting to digitally enhance it. For instance, even though financial technologies and digital financial inclusion have gained significant traction on the continent, agents —third party “microentrepreneurs” embedded within financially underserved and excluded communities are authorized to facilitate transactions (typically handled offline) on behalf of service providers26 — remain a critical driver of uptake and customer retention, be it for mobile money27 or for banking.28 Similarly, in e-commerce, the “informal” nature of relations and work has necessitated startups operating on the continent to engage agents who guide first time buyers, those sceptical of online transactions and even those without internet-enabled devices to make purchases, especially in rural areas.29 In other words, the social order that upholds Africa’s informal sectors,30 including how productivity is shaped,31 warrants better understanding to shape the assumptions that will shape both AI innovations and governance for this critical domain in the African economic landscape.
14Rather than apply broad claims of AI’s disruptive potential, instead we ought to be asking, with humility and curiosity, what AI can do for Africa’s informal sector. Can it— in line with the proposed policy narrative shift help “informality serve as stepping stone”, either to formalization24 or as a “cheap experimental environment” for entrepreneurs,32 or revolutionize it altogether? How do the efficiency and productivity gains that AI purportedly offer become relevant and impactful in this work paradigm? Can AI-enabled work advance social and legal protections such as minimum wage and safe workplaces for informal sector workers leading to improved livelihoods? And how will the informal sector workers be engaged in deliberating on and shaping AI’s role therein? This will necessitate co-creating policy with the people and enterprises in this sector, rather than creating for them.
15Given the outsize role that the informal sector plays in Africa’s skills and labor market, it is important to pay attention to AI’s impacts therein, which may not lend itself to the prevailing narratives and assumptions about skilling and the future of work. Skilling initiatives and educational reforms are one half of the equation, for which many digital and AI initiatives exist. However, meaningful, quality jobs are the other important half, that warrant deeper deliberation and policy reorientation. Doubling down on skilling Africans, without meaningfully tackling the systemic barriers to creating and sustaining enterprises (AI-enabled or otherwise) and jobs is a policy mirage that undermines the ambitious local, regional and continental development visions and goals. Despite calls for Africa’s youth to embrace entrepreneurship, there remain significant challenges spanning policy, governance, regulation and sociocultural dynamics that tend to bound the possibilities of growth and sustainability therein. That most African workers — including many who are otherwise highly skilled and formally educated — end up working in the informal sector is indicative of the fact that the continent doesn’t have a skills issue; it has a jobs issue. Reforming the pathways for job creation and growth of enterprises will create the pathways for AI to deliver its potential, be it in the formal or informal sector. If indeed there is a risk of an AI divide, where Africans are left behind, it won’t be because they aren’t skilled; it will be because public policy and governance misdiagnosed the problem.
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