The Global South Must Shift the AI Conversation from Adoption to Readiness
We cannot afford to participate in the artificial intelligence revolution as though we were entering it from the same starting line as those who built its foundations.
As the Global South, we are joining the artificial intelligence conversation as though business were proceeding as usual. Governments are announcing AI strategies, institutions are exploring AI integration, universities are revising curricula, and businesses are racing to adopt AI-powered systems. Yet beneath this enthusiasm lies an uncomfortable question: Are we structurally ready to participate in the AI revolution on equitable terms, or are we simply responding to a technological agenda largely shaped by others?
This is not an argument against AI adoption. Nor is it an argument for technological isolationism. It is an argument for changing the order of our priorities.
Before we ask how quickly the Global South can adopt AI, we must ask how prepared it is to absorb, govern, adapt, develop, and derive value from it.
1. We are entering an asymmetric technological order
The global AI landscape is not a level playing field. Countries enter this conversation with vastly different levels of technological infrastructure, computing capacity, capital, research investment, data resources, institutional maturity, and human capability.
A country with reliable electricity, advanced digital infrastructure, well-funded research institutions, mature data ecosystems, strong innovation financing, and an experienced AI workforce does not face the same adoption challenges as a country still grappling with unreliable power, fragmented databases, limited computational resources, skills shortages, and weak institutional coordination.
Yet international AI conversations often place these countries under the same umbrella of adoption, innovation, and competitiveness.
This creates a fundamental asymmetry: we are being encouraged to compete in a technological race in which the starting conditions, productive assets, and capacity to absorb the risks are unequally distributed.
The asymmetry extends beyond infrastructure. It concerns who develops the foundational models, who owns the computing infrastructure, whose languages and cultural contexts are represented in training data, who determines the technical standards, who captures the intellectual property, and who ultimately receives the economic returns.
The Global South risks becoming a vast market for AI consumption without acquiring a proportionate capacity to develop the technologies, influence their governance, or capture their productive value.
This is where the conversation must become more critical. We must look beyond access to AI tools and examine the distribution of technological power.
2. Readiness is not a footnote to adoption
AI readiness is often treated as a preliminary consideration before the real business of adoption begins. For the Global South, readiness should be the central conversation.
There are at least six interconnected dimensions that deserve serious attention.
- Skills readiness
Do we have enough people with the technical, analytical, managerial, and domain-specific capabilities required to deploy AI productively? Beyond AI literacy, do our institutions possess the skills to evaluate AI outputs, redesign workflows, manage AI systems, and develop locally relevant applications?
- Infrastructure readiness
AI depends on reliable electricity, connectivity, computing resources, cloud access, cybersecurity, and affordable digital infrastructure. Where these foundations are weak or expensive, the cost of AI adoption can become an additional structural disadvantage.
- Data readiness
AI is only as useful as the data ecosystems that support its application. Fragmented databases, poor data quality, incompatible systems, limited digitization, weak data governance, and underrepresentation of local languages and contexts constrain what AI can reliably deliver.
- Institutional readiness
Institutions need clear governance frameworks, accountable leadership, procurement capacity, change management, cybersecurity controls, and policies governing the responsible use of AI. Purchasing a tool does not automatically create an institution capable of using it effectively.
- Productive and innovation readiness
Can local firms, researchers, universities, and entrepreneurs transform AI into locally owned products, services, intellectual property, and employment? Or will the region primarily supply users, data, raw materials, and markets for innovations commercialized elsewhere?
- Regulatory and strategic readiness
Can governments negotiate technology contracts, protect sensitive data, assess systemic risks, enforce accountability, and shape AI standards? Do policymakers have sufficient independent expertise to scrutinize the commercial and geopolitical interests of the companies whose systems they intend to adopt?
These dimensions cannot be addressed independently. Skills without infrastructure are constrained. Infrastructure without capable institutions can be underutilized. Data without governance creates risks. AI tools without workflow redesign may simply digitize existing inefficiencies.
Readiness is a system of interdependent capabilities, not a checklist of isolated prerequisites.
3. The danger of technological dependency disguised as progress
One of the most consequential risks facing the Global South is the possibility that AI adoption will deepen existing dependencies rather than overcome them.
Consider an institution that adopts a foreign AI platform to manage essential functions. It may gain immediate productivity benefits. However, if its data, computing, technical expertise, critical workflows, and long-term operational continuity depend on external providers, it may also acquire new forms of technological dependence.
Who controls the infrastructure? Who can access or process the data? What happens when prices change, services become unavailable, contracts are terminated, or geopolitical tensions affect access? Can the institution migrate to another provider without prohibitive cost? Can local professionals maintain, audit, or adapt the system?
These are not reasons to reject external technologies. International collaboration and imported technologies can accelerate development. The concern is adoption without bargaining power, without alternatives, and without a deliberate strategy for building domestic capabilities.
Technological sovereignty does not necessarily mean developing every component locally. It means possessing enough capability, choice, governance, and negotiating power to avoid being trapped in arrangements that undermine national or institutional interests.
For the Global South, the objective should therefore be neither technological autarky nor passive dependence. It should be strategic interdependence.
4. The hegemonic dimension of AI
The AI conversation also has a geopolitical dimension that cannot be ignored.
Technological hegemony emerges when a relatively small number of actors possess disproportionate influence over the infrastructure, standards, models, capital, intellectual property, and commercial ecosystems on which others increasingly depend.
When those actors are concentrated in a few countries and corporations, their technical and commercial decisions can have consequences far beyond their immediate markets.
This influence need not involve an explicit political agenda. It can operate through ordinary market mechanisms: platform lock-in, proprietary systems, pricing structures, cloud dependencies, language priorities, intellectual property arrangements, and the ability to determine which technologies become industry standards.
There is also an epistemic dimension. If AI systems predominantly reflect the data, languages, historical records, research priorities, and cultural assumptions of better-represented societies, then the Global South risks consuming intelligence infrastructures that insufficiently reflect its own realities.
Our local problems may be interpreted through external assumptions. Our languages may receive less support. Our researchers may depend on models they cannot meaningfully inspect or influence. Our institutions may become efficient at implementing solutions while remaining dependent on others to define the problems worth solving.
This is why the issue is not simply whether AI works. It is also whose priorities AI serves, whose knowledge it privileges, whose interests it advances, and who has the power to change its direction.
The challenge for the Global South is to participate in shaping the technological order, rather than merely adapting to the order shaped by others.
5. We must confront the asymmetry in value creation and value capture
AI can create enormous economic value, but the distribution of that value is not automatic or necessarily equitable.
The Global South may contribute users, domain knowledge, training and evaluation labor, natural resources, data, and expanding consumer markets. Yet the most profitable layers of the value chain, advanced computing, foundational models, intellectual property, cloud infrastructure, and global distribution, may remain concentrated elsewhere.
This raises an important question: can our economies translate AI adoption into locally retained economic value?
If organizations use AI to reduce labor costs but do not reinvest in workforce capabilities, new enterprises, research, or higher-value activities, productivity gains may not translate into broad-based development.
If universities encourage students to use AI but do not develop the capacity to build AI systems, they may improve access to intelligence without strengthening domestic technological production.
If governments digitize services through externally controlled platforms without developing procurement expertise, data governance, and technical capacity, they may improve service delivery while increasing strategic dependency.
The critical distinction is between AI utilization and AI capability accumulation.
Utilization gives us the immediate benefit of using existing technologies. Capability accumulation builds the skills, infrastructure, research institutions, businesses, intellectual property, and bargaining power needed to participate more fully in future technological developments.
The Global South needs both, but an agenda dominated by utilization risks leaving the structural distribution of technological power unchanged.
6. The workforce question: readiness before displacement
The workforce dimension deserves particular attention.
AI is changing the composition of work, the skills required for employment, and the relationship between education and economic opportunity. However, countries enter this transition with different labor-market structures, levels of social protection, education systems, and capacities for reskilling.
In economies where formal employment opportunities are already limited, the effects of automation cannot be evaluated solely through productivity statistics. The relevant questions include whether displaced workers can transition into new occupations, whether businesses can create higher-value jobs, and whether education and training systems can respond at the speed required.
We should not promise that every job eliminated by technology will be replaced by a better one. Nor should we assume that AI will automatically create sufficient new employment to absorb those who are displaced.
The Global South must therefore align AI deployment with workforce readiness, enterprise development, occupational transitions, and institutional capacity.
This calls for a shift from discussing AI literacy in isolation to examining workforce transformation readiness: whether workers, employers, education systems, and governments can collectively navigate changes in the organization and distribution of work.
7. A different agenda: from AI adoption to AI readiness and capability
What, then, should the Global South prioritize?
It needs a deliberate readiness agenda that links AI strategy to measurable institutional and national capabilities.
- Skills
- Infrastructure
- Data
- Institutions
- Innovation
- Governance
- Economic value
- Strategic autonomy
These questions should inform national AI strategies, public-sector procurement, education reform, university research, workforce planning, and private-sector investment.
Readiness assessments should also be practical rather than ceremonial. Governments and institutions need baseline measurements, prioritized investments, implementation timelines, responsible actors, and indicators that reveal whether their capabilities are improving.
The objective is not to postpone adoption until every weakness has been resolved. That would be unrealistic and could create another form of exclusion.
Instead, adoption should proceed alongside readiness-building, with the scale and criticality of each deployment matched to the institution’s capabilities and the risks involved.
Low-risk applications can help institutions learn and improve. High-stakes applications affecting public services, employment, finance, health, or sensitive information demand stronger safeguards and institutional capacity.
This is a strategy of sequenced adoption, continuous learning, and deliberate capability accumulation.
8. The Global South needs a voice in defining the questions
There is another subtle but important issue: who defines the AI agenda itself?
If the Global South’s participation is limited to asking how it can catch up, how quickly it can adopt AI, or how it can implement technologies developed elsewhere, its contribution remains reactive.
We must also ask questions that arise from our own structural circumstances:
- How can AI be used to address foundational development constraints rather than merely replicate existing technological trajectories?
- What institutional capabilities must be developed before AI becomes embedded in critical public services?
- How can local knowledge, languages, intellectual property, and research capacity be incorporated into the AI economy?
- What forms of international collaboration strengthen local capability instead of creating permanent dependence?
- How can AI productivity gains be converted into enterprise growth, better public services, and meaningful employment?
- What negotiating mechanisms can enable countries with limited technological power to secure fairer terms from dominant providers?
- How can African universities move from predominantly consuming AI-enabled knowledge to producing locally grounded research, applications, and intellectual property?
These questions do not reject the global AI conversation. They make it more representative of the world in which AI is being deployed.
The Global South must be a participant in defining the questions, not merely a respondent to questions formulated elsewhere.
Conclusion: Readiness is a question of power, not just preparedness
The Global South’s AI challenge is not simply a shortage of skills, infrastructure, data, or institutional capacity. Those are immediate constraints, but they sit within a wider international system characterized by unequal technological capabilities and unequal power to determine how value is created and distributed.
That is why the readiness conversation matters.
Without it, AI strategies may become adoption plans detached from institutional realities. Digital transformation may become technological dependency. Productivity gains may accrue disproportionately to external providers. And the promise of AI-enabled development may coexist with persistent structural disadvantage.
We should embrace experimentation, innovation, and the productive use of AI. But we must also be deliberate about the conditions under which adoption takes place and the capabilities it leaves behind.
The objective is not merely to ensure that the Global South can use AI. It is to ensure that it can shape AI, govern AI, build with AI, derive value from AI, and negotiate its place in the technological order with greater agency.
The most important question is therefore not simply whether the Global South is ready for AI.
It is whether we are building the capabilities required to participate in the AI economy without reproducing the very asymmetries that have historically constrained our development.
We cannot close a structural capability gap through adoption enthusiasm alone. We must build readiness into the architecture of our AI ambitions.
