How the Global South Can Shape the Future of AI Development and Governance
As artificial intelligence reshapes economies and institutions, persistent gaps in access to computing infrastructure, locally relevant data and digital skills are shaping who can meaningfully participate in its development. But closing the digital divide is not only a question of access. It is also a question of who gets a seat at the table to help shape the technology itself.
On September 21, Meridian International Center and Primus Partners convened Grounding the Frontier: A Global South AI Framework at the Consulate General of India in New York on the sidelines of the 81st United Nations General Assembly. The discussion brought together leaders from government, industry and the multilateral community to examine how locally grounded, resource-efficient AI can move from experimentation to deployment at scale, while positioning the Global South as a co-architect of AI development and governance.
The convening also marked the release of Meridian’s AI Horizons: Dialogues on AI in the Global South, a synthesis of Meridian’s 2026 AI Horizons roundtables, and Primus Partners’ Built Lean, Built to Scale: An Artificial Intelligence Compendium from the Global South, which draws upon case studies and interviews offering on-the-ground insight to how Global South nations are leading the shift in AI governance.
Here are the top takeaways:
1. The Global South should be viewed as a partner in both AI design and governance.
Far from acting solely as consumers and recipients of technology, the Global South is already adapting AI to solve problems and deserves a seat at the table both as a contributor to AI development and a voice in AI governance discussions. Building AI solutions that work in different countries and cultural contexts starts by including the Global South at the design stage and investing in their development capacity. Rather than adapting technologies after they are built, local stakeholders can shape models around their own languages, cultures, regulations and needs -- provided they have the necessary access to AI inputs such as connectivity, compute, data, and more. Engaging Global South nations as partners and peers in AI model and tool development from the outset can produce AI that is more relevant to the environments where it will ultimately be used and tailored to deliver solutions to unique and local problems. Furthermore, the Global South with its vast population of current and potential users and developers ought to bring into the AI governance discussion and decision-making process.
2. AI deployment must be built around solving a problem in its local context.
Technology should be built around the problem, rather than forcing a problem to fit a general desire to use AI, and this requires an engaged ecosystem of stakeholders. Innovation alone is not enough: widespread digitalization requires appropriate institutions and clearly defined incentives to translate technological capability into meaningful impact. Ability to rapidly scale is also key to a solution’s impact and should be part of design and deployment discussions. In countries such as India, where AI-driven solutions in sectors such as government, health, and agriculture need to serve populations at enormous scale, identifying constraints and designing for widespread use are critical from the beginning.
3. Local data is essential to building AI that works in local contexts.
The sophistication of an AI model matters less than the relevance of its dataset to its use case. Models built with localized and sector-specific data are better positioned to account for differences across countries, cultures and industries, particularly in areas such as healthcare, agriculture, education, and governance where inaccurate results can carry significant consequences. However, frontier AI models are largely trained on data representing a limited selection of languages and cultures, and local or regional data is often limited in availability. Building locally sensitive AI will require investment in the availability, quality, and governance of the data behind it. Countries must balance the need and cost to prepare local data for AI to use while maintaining appropriate standards of data privacy and protection.
4. AI capacity depends on the entire ecosystem.
Countries throughout the Global South are actively developing national strategies that prioritize the diffusion of AI skillsets, literacy, and utilization across the wider population as an alternative to strictly competing on the basis of frontier model development. To achieve AI capacity at scale, governments can strengthen infrastructure and financing, industry can invest in talent and local providers, startups can translate technology into practical applications, and international institutions can help build capacity and establish guidelines. Aligning the efforts of these actors creates the environment in which the people affected by advancements in AI remain part of its development, with impact measured by how effectively technology improves lives rather than technological development alone.
5. Trust will determine whether AI can scale sustainably.
AI adoption depends on trust in both AI itself and the institutions responsible for deploying and governing it. That trust can look different across sectors, encompassing how data is used, whether institutions are viewed as credible, and whether communities retain meaningful control over technologies deployed locally. Genuine stakeholder participation, institutional independence and on-the-ground capacity building can help create the conditions for AI systems that people are willing and able to use.
Project summary
| How the Global South Can Shape the Future of AI Development and Governance | September 2026 |
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