AI Strategies and the Informal Economy: Africa’s Job Creation Test

This job creation deficit is compounded by the uncertainty surrounding AI’s impact on the workforce. In the short term, with productivity gains from AI currently concentrated in desk-based professional roles, Africa’s informal sectors will be buffered from AI-related job losses. In the medium and long term, Africa’s informal sectors are in danger of realizing only limited gains from the AI revolution or missing out completely. The creative promise of AI, if intentionally integrated into the informal, small-scale business economy, could facilitate the entrepreneurship and innovation that are foundational to the continent’s long-term economic growth. Projected digital and AI jobs are gig-based roles that could absorb informal workers, provided the right infrastructure and training ecosystems are put in place. The African Development Bank estimates that AI could account for nearly one-fifth of Africa’s total GDP growth by 2035, creating up to 40 million jobs across the agriculture, healthcare, retail, finance, and manufacturing sectors. Whether AI delivers on this potential will depend on how and whether AI strategies address Africa’s informal workers.

Examining the Job Creation and Workforce Potential of AI

While Africa’s vast informality may limit exposure to job automation, it may also make the continent less able to capture AI’s technological gains. Overall, however, public and policymaker perceptions of AI across Africa are positive. The growing consensus in African policy circles is that AI can be leveraged to expand productivity and deliver jobs in critical sectors. Similarly, in a recent survey, half of African respondents were optimistic that AI will improve their work opportunities in the short and medium term.

Policies at the continental and national levels clearly express this ambition for AI-driven economic growth. The African Union’s Continental AI Strategy outlines the potential for adoption to accelerate its Agenda 2063 by deploying AI in critical sectors such as agriculture, education, and health. Relatedly, national AI frameworks in some of the continent’s largest economies, such as Kenya, Nigeria, and South Africa, all frame AI as a tool for job creation, economic diversification, and skills development.

The CSIS Africa Program is partnering with the Global Center for AI Governance with support from Cisco to conduct cross-country research in Kenya, Nigeria, and South Africa on the opportunities and challenges of national AI strategies for job creation. The report will review national AI policies in comparison to the respective governments’ stated job creation goals. While each of these strategies reflects genuine ambition, they also recognize the structural challenges, such as limited digital infrastructure and a digital access divide. Navigating these constraints requires policy responses tailored to each country’s unique economic structure, workforce composition, and infrastructure readiness. Critical in this equation is the question of how to engage the informal economy.

Identifying Focus Areas and Gaps in National AI Strategies

African policymakers have shown significant commitment to AI adoption, demonstrated by stronger performance compared to previous years across key levers of AI research, capacity building, and compute infrastructure. Between 2023 and 2025, Kenya, Nigeria, and South Africa rose 33, 31, and 12 places, respectively, on the Oxford Government AI Readiness Index.

Beyond adoption, AI policies at both the continental and national levels show a dedicated focus on homegrown economic development through an AI-driven economy. As policy is put into practice, priority is being given to delivering capacity-building programs to support targeted AI applications and development. Data from the 2026 Global Index on Responsible AI shows that AI literacy, reskilling and upskilling initiatives, and public sector skills development are areas with the highest number of AI policy implementation programs supported by African governments. In addition to skills investment, a strong national strategy focused on job creation and economic gains will also include local infrastructure development, private partnerships, and transition support for sectors and industries that spur AI use and integration, including for workers.

Across Kenya, Nigeria, and South Africa, AI strategies generally show a clear commitment to supply-side policies in the form of skills investment, local infrastructure, and partnerships with private technology companies, while also reflecting the variations in their respective economic conditions. For instance, the Nigerian government has largely focused on extensive upskilling programs and investment in homegrown AI research clusters. Its flagship program, the 3 Million Technical Talent (3MTT), aims to train 3 million Nigerians in core digital technology skills, including AI, with over 135,000 participants so far. Extensions of the 3MTT into AI-specific skills have been supported by the private sector, including a partnership with Microsoft to train 1 million Nigerians in AI skills. Complementing these are research and development (R&D) activities centered around universities and government research clusters, such as the repurposing of the National Centre for AI and Robotics to carry out modern large language model research and a partnership with the United Nations Development Programme (UNDP) to establish regional AI clusters in six universities across the country.

Kenya’s strategy focuses heavily on infrastructure investment and local innovations. Its five-year AI Strategy, backed by a $1 billion (KES 152 billion) implementation budget, positions AI within the country’s broader digital economy master plan, which focuses on leveraging technology to accelerate economic growth through infrastructure expansion, government e-services, and skills upgrades. A large part of the AI budget seeks to develop connectivity, data centers, and innovation hubs. This infrastructure is designed to complement Kenya’s already-strategic position in Africa’s digital economy, with an established track record of attracting foreign digitally focused investment. Nairobi alone is home to approximately 500 startups and serves as a major regional hub for Google and Microsoft. Additionally, Kenya has made strides to prioritize upskilling in the public sector. The Africa Center of Competence for Digital and Artificial Intelligence Skilling, jointly established with UNDP and Microsoft, aims to incubate public sector AI innovation, train over 1,000 AI specialists, and expand AI literacy in Kenyan schools.

South Africa’s National AI Policy framework draft, previously released for public comment, outlines a more detailed approach to the country’s economic aspirations in AI, with “AI for Inclusive Growth and Job Creation” as one of its six strategic pillars. While the implementation of this policy largely remains in the balance given its recent recall by the ministry, the framework has emerged within strong institutional capacity-building initiatives, such as AI skills development partnerships with major tech companies, and R&D initiatives. For example, the Department of Communications and Digital Technologies is funding the AI Institute of South Africa, which hosts university hubs actively driving localized AI projects and workforce reskilling in critical sectors such as mining, agriculture, and government administration.

For all their strengths, however, these three countries’ AI strategies and implementation initiatives lack concrete policies and measures to spur AI integration in the informal sector. Particularly for countries such as Nigeria and Kenya, where the vast majority of economic activity is informal, an overtly industry-focused AI policy implementation will serve only a limited proportion of the economy.

A Closer Look at Informality in Kenya, Nigeria, and South Africa

The informal sector accounts for 93 percent of employment in Nigeria, 84 percent in Kenya, and a much smaller 21 percent in South Africa, based on the latest data available. The informal economy encompasses business activities that fall outside of the regulatory and legal environment governing businesses. This means they largely go untaxed and unregulated.

In both Kenya and Nigeria, the concentration of informal employment is in agriculture, followed by trade and commerce, reflecting the continued reliance of informal workers on smallholder farming and small-scale retail. While South Africa’s informal workforce is significantly smaller, it is also concentrated in small-scale retail, street and market vending, artisanal manufacturing, and other industries such as domestic work and childcare. Examples of informal work include farmers with small plots of land growing food for their own households or for sale at local markets, roadside and market vendors, local artisans and handymen, and independent taxi, bus, and truck drivers, to name a few.

Given the manual-, service-, and trade-based focus of informal work, it is not yet one of the focus areas for AI applications. While informal workers can and do deploy AI tools to assist their businesses with rote tasks such as simple accounting, branding, and social media posts, this use is relatively limited compared to applications in other sectors.

What Are the Options for Informal Economic Integration with National AI Strategies?

Despite the current orientation of national AI strategies toward the formal sector and supply-side interventions such as infrastructure development, talent pipelines, R&D, and startup ecosystems, there remains room to pivot toward the informal majority. Acknowledging the importance of the informal sector to the livelihoods of citizens in these countries and treating the sector as a distinct policy target opens a range of options for bringing the stated goals of job creation, economic diversification, and skills development to the majority.

First, an informally focused AI policy would be designed with sectors such as smallholder agriculture, transportation, artisanal manufacturing, and retail in mind. This means recognizing the local limitations that accelerate the digital divide. AI tools that take the barriers seriously would be offline-capable, incorporate voice- and image-first options, and work across multiple local languages.

Second, implementation road maps to meet policy goals would focus on sector-specific, complementary applications. There may be limited immediate utility of AI tools for direct labor in informal jobs, such as those of an artisanal furniture manufacturer, a smallholder farmer, or a truck driver. However, complementary applications that enable and streamline business, such as alternative data credit, crop diagnostics, and freight matching, are genuinely useful.

Third, resources would flow to informal operators to encourage engagement and spur experimentation. Subsidies, tax incentives, and grants that can spur demand and use in the informal sector, as well as startup and small and medium enterprise (SME) support, can be combined with new forms of credit and blended finance to unlock greater engagement with AI in the informal sector. This is critical to addressing the demand-side deficit.

Fourth, labor and economic data needs to include questions on AI adoption and labor transition in informal sectors. Embedding these questions into the surveys that national statistics agencies already run creates an evidence base to inform iterative implementation. Combining public data with privately compiled workforce data, which would enable more real-time, actionable insights, would be even better.

Fifth, underpinning all of this must be data governance regimes that are accessible, transparent, and understandable to the people engaging with these tools. Such governance frameworks and the AI tools they underpin must also be interoperable across national boundaries and jurisdictions. Cross-border intra-African trade is currently estimated to be between 7 and 16 percent of all trade flows. Therefore, creating the enabling conditions for credit, payments, and data tools to flow and function seamlessly across borders is an essential element of inclusive economic engagement.

None of these pivots requires new strategies, frameworks, or policies. The AI policies and broader digital strategies of these countries already include elements that can be supplemented by implementation choices focused on informal sectors. Kenya’s strategy identifies micro and small enterprises as a priority sector and aligns with the Bottom-Up Economic Transformation Agenda, which can create opportunities for workforce transition frameworks and for identifying protections for informal and gig workers. Nigeria can turn its commitment to the informal economy by adding an informal training track to the 3MTT program. South Africa also already has a pillar of its AI policy on AI for inclusive growth and job creation, which can be strengthened through data-driven engagement with the country’s largest informal sectors, such as retail and transportation. Made as concrete, measurable commitments, those choices would build the momentum for ongoing enhancement and engagement with the citizens these sectors sustain.

Ultimately, for most African economies, a truly meaningful AI pathway must target traders, farmers, drivers, artisans, and others who are largely excluded from the current AI use trajectory. Across Africa, informality is the test of whether AI creates broad-based work that achieves economic development goals and lives up to its promises or simply deepens an existing divide.

Going beyond informality, the forthcoming Global Center for AI Governance and CSIS Africa program report takes an even deeper look at alignment between economic sectors, workforces, AI integration, and policy frameworks in Kenya, Nigeria, and South Africa.

Ayantola Alayande is a researcher at the Global Center on AI Governance. Catherine Nzuki is an associate fellow with the Africa Program at the Center for Strategic and International Studies (CSIS) in Washington, D.C. Aaron Stanley is deputy director and fellow in the Africa Program at CSIS.