The Reskilling State: A New Compact for Work in the Age of AI
The rise of artificial intelligence presents a familiar challenge in a new guise: how to cultivate human capability at the pace of technological change. For nations across Africa, this is not merely an economic question but a societal one, centred on inclusive growth. The risk is that AI amplifies existing inequalities, leaving the vast majority of workers behind. Yet the opportunity is to purposefully steer technological adoption toward shared prosperity. Realising this future requires a new social compact for workplace learning and adaptation — a ‘reskilling state’ founded not on rigid, top-down plans but on a dynamic partnership among government, industry, educators, and workers themselves. This compact must be geared for a world where skills are portable, learning is continuous, and opportunity is accessible to all, including the continent’s millions of informal workers and small-business entrepreneurs who form the backbone of the economy.
Key Takeaways
- Rather than trying to predict future jobs, African governments should act as strategic conveners of the skills ecosystem, fostering collaboration and setting standards.
- A new compact for reskilling must be designed for the reality of African economies, prioritising the continent’s vast informal workforce and small enterprises.
- Portable, interoperable credentialing systems are the essential infrastructure for workers to capture and communicate the value of their skills throughout their careers.
- Shared financing models and a strong focus on outcome-based accountability are critical to ensure that reskilling initiatives are effective, relevant, and sustainably funded.
The End of the ‘Train-and-Pray’ Model
For decades, national skills development has often followed a familiar logic: governments fund technical colleges and universities to train young people for a set of officially sanctioned occupations in the hope they find suitable work. In an age of rapid technological flux, this model is no longer fit for purpose. The swift evolution of AI means the specific skills demanded by the market are constantly changing. An analysis by the International Labour Organization (ILO) suggests that AI’s primary impact is more likely to be occupational transformation than outright job replacement, altering tasks within jobs rather than simply eliminating entire roles. This finding underscores the urgent need for continuous, on-the-job adaptation rather than one-off, front-loaded education. The focus must shift from preparing for a first job to preparing for a lifetime of learning and career transitions.
In this dynamic environment, a top-down, state-led attempt to forecast the jobs of the future is destined to fail. The World Bank advises policymakers to pursue an adaptive, evidence-led AI strategy that can evolve with new information, rather than launching isolated and rigid initiatives. The new role for the state is not as a central planner but as a strategic convener and ecosystem architect. This means establishing the infrastructure, incentives, and standards that allow workers, employers, and training providers to respond deftly to emerging needs. A core function is to foster robust social dialogue, creating a structured forum where employers can articulate their evolving requirements and where worker representatives can ensure that reskilling pathways lead to decent and dignified work. This dialogue is the feedback loop that makes an adaptive strategy possible. Without it, government investments risk being misaligned with market realities, and technological change can easily bypass worker protections. As the African Union’s continental AI strategy highlights, developing skills is a central pillar of its vision for a human-centric and inclusive AI-powered future, intrinsically linking technological progress with human development.
Prioritising the Continent’s Economic Engine
Any credible reskilling strategy in an African context must be designed for the reality of its labour markets, where small and medium-sized enterprises (SMEs) and informal work predominate. These sectors are the continent’s economic engine, but they are also the most vulnerable to being excluded from the benefits of digital transformation. As the ILO notes, in developing economies, the digital divide constrains both the potential disruption and the potential gains from AI. Workers in less-connected environments have fewer opportunities to leverage AI tools or to access digital training platforms that could enhance their productivity and incomes. For an SME, the barrier is not just a lack of connectivity but also a lack of capital to invest in new technologies and a lack of time for owners and employees to pursue formal training courses.
Therefore, the new reskilling compact must actively design for inclusion from the outset. This means moving training out of the exclusive domain of formal institutions and into more accessible formats and delivery institutions. Training must be brought to where people work, through mobile-first micro-learning modules that can be completed in short bursts, peer-to-peer knowledge sharing networks within market associations, and apprenticeship models adapted for small business clusters and agricultural cooperatives. Instead of requiring a worker to leave their business for weeks, training could be delivered via interactive voice response on a basic mobile phone or through a trusted local agent. It requires financing mechanisms that do not depend on formal employment relationships, such as individual learning accounts or sector-wide training levies that pool resources for collective benefit, allowing an informal artisan or smallholder farmer to access the same quality of skills development as a corporate employee. Critically, it involves developing clear and accessible pathways for informal workers to have their existing skills recognised and certified—a process often called Recognition of Prior Learning (RPL). This is not merely about validation but about creating a bridge to more formal opportunities, better credit access, and greater economic security. The World Bank has long emphasised that human capital is central to inclusive growth, and this can only be achieved by meeting workers where they are, both geographically and economically, with solutions tailored to the specific constraints and opportunities of the informal economy.
Building the Rails for Lifelong Learning
Skills are only truly valuable if they are visible, portable, and trusted. A worker may complete numerous training courses over their career—a short online module on digital marketing, a government-sponsored apprenticeship in solar panel installation, a workplace safety certificate from their employer—but without a reliable way to signal those achievements to new employers or clients, their economic mobility remains constrained. A foundational element of the reskilling state is the creation of interoperable learning records, often called a ‘comprehensive learner record’ or ‘digital skills passport’. This is not a centralised government database but a piece of public-good infrastructure based on open standards, allowing individuals to accumulate verifiable credentials from a wide array of providers in a single, secure digital wallet that they own and manage.
As the OECD notes in a global context, skills only translate into economic mobility when institutions effectively connect learning to opportunity. Interoperable credentials are this connective tissue. For employers, they reduce hiring friction and provide a much more reliable and granular signal of a candidate’s capabilities than a traditional degree alone. They can see not just a diploma, but a detailed record of specific competencies. For workers, particularly those without formal degrees, such a system is transformative. It makes their investments in learning worthwhile by ensuring that newly acquired skills are recognised across a sector, a nation, or even a region, breaking the dependence on a single employer or location. This portability is especially crucial for project-based ‘gig’ workers, seasonal agricultural workers, and informal traders. For the ecosystem as a whole, the aggregated, anonymised data from these credentials create a common language for skills that allows training providers to align their offerings with real-world, real-time demand, a key component of the adaptive strategy the World Bank advocates. This system of portable, trusted credentials is an essential public good for the AI age, enabling a more fluid and responsive labour market.
A Shared Burden, A Shared Benefit
Financing the Future of Skills
Reskilling an entire workforce is an expensive undertaking that demands a sustainable and equitable financing model. No single actor—government, employer, or individual—can or should bear the cost alone. An effective compact requires a shared financing model that reflects this shared responsibility and mutual benefit. The government’s primary role is not to be the sole funder but a strategic catalyst. This involves providing initial seed funding for innovative training programmes and creating powerful fiscal incentives, such as tax credits for employers who invest in training their workers. To ensure these incentives promote equity, they could be structured to give greater rewards for training lower-skilled employees, older workers, or women, groups often left out of corporate training budgets. Industry levies, where all firms in a specific sector contribute a small percentage of payroll to a central training fund, can overcome the classic free-rider problem, where companies are reluctant to train workers who might then be poached by competitors. These funds, co-managed by employer associations and worker representatives through robust social dialogue, ensure that training is directly relevant to the sector’s needs. Individuals, too, have a role to play, but their contributions must be equitable. Mechanisms like income-share agreements, where a training provider is repaid a percentage of the learner’s future earnings, can be effective but must be carefully regulated and linked to demonstrable earnings gains to be just and avoid predatory practices.
Designing for Digital Equity
The promise of AI-driven productivity and growth cannot be realised if the foundational building blocks are not in place for everyone. The World Bank’s 2025 Digital Progress and Trends Report identifies four key pillars for AI readiness: connectivity, computational power, contextual data, and competency. Crucially, it finds that these foundations are unevenly distributed, both between and within countries, creating pockets of ‘AI deserts’. An effective skills strategy cannot ignore these foundational gaps. Furthermore, a persistent gender-based digital skills divide, as highlighted in another World Bank report on Sub-Saharan Africa, threatens to exclude women from the opportunities of the digital economy. The report finds that gendered barriers must be proactively addressed in policy design, not treated as an afterthought. A reskilling strategy that is not intentionally inclusive will inevitably perpetuate existing inequalities. This means ensuring that training programmes are offered in safe, accessible locations and at flexible times that accommodate women’s traditional caregiving responsibilities. It requires that programmes are paired with wrap-around services like mentorship, childcare support, and professional networks for female participants. Tackling the digital divide, therefore, is not just about technology; it is about addressing the social structures that prevent equitable access to it.
Accountability for Outcomes
This entire financial and strategic partnership must be underpinned by a radical shift towards outcome-based accountability. For too long, public and private funds for training have been allocated based on inputs, such as enrolment numbers or hours of instruction, with little regard for whether graduates acquire relevant skills or find better, more secure employment. An effective and just approach ties a significant portion of funding to verified results. These outcomes must be defined multi-dimensionally: skills mastery assessed through practical demonstrations, the attainment of industry-recognised certifications stored in a portable credential wallet, and, most importantly, improved employment and earnings outcomes for participants. This requires robust but privacy-preserving data systems to track learner progress and link training data with labour market information, such as tax records or employment surveys. This focus on results is a key component of the adaptive strategy advocated by the World Bank, as it creates the crucial feedback loop that allows for continuous improvement. It ensures that public and private investments are systematically channelled towards programmes and providers that actually work, while those that fail to deliver tangible value for learners are reformed or replaced. This performance-based approach instils discipline in the training market and ensures that the ultimate goal—enhanced economic mobility for workers—remains the central measure of success.
Conclusion: A Compact for Human Capability
The age of AI does not have to be an era of deepening inequality for Africa. By embracing a new, collaborative approach to skills development, African nations can turn a moment of technological disruption into a catalyst for widespread empowerment and inclusive prosperity. The ‘reskilling state’ is not an all-powerful bureaucracy dictating the future of work from the capital city, but rather a nimble facilitator of a decentralised, dynamic learning ecosystem. It is a compact built on shared responsibility: the state sets the strategic direction, provides foundational infrastructure, and ensures equity; employers articulate needs and provide vital on-the-job learning opportunities through structured social dialogue; educators and innovators deliver relevant, flexible training through diverse and accessible institutions; and workers themselves commit to a journey of lifelong adaptation. By grounding this partnership in a practical focus on informal workers and SMEs, building the interoperable infrastructure for portable skills, and holding all parties accountable for tangible, life-changing outcomes, this new compact can provide the essential foundation for a more prosperous, resilient, and equitable future of work for all.