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Few fields—in any sector, at any point in history—have been as enveloped in hype as AI. This has many distorting effects. Hype inflates investments and valuations, and it stokes both utopian expectations and dystopian anxieties. In addition, hype also crowds out the kind of grounded, evidence-based analysis that should inform how states and others govern AI: the questions they ask, the trade-offs they weigh, and the priorities they settle on in allocating resources and attention. When it comes to policymaking in particular, hype leads to a version of what Esther Duflo and Abhijit Banerjee, writing of development policy, call “the : ideology, ignorance, inertia.”��

A key goal of this report is to push against the “three Is”: to slow down, look closely at what is actually happening on the ground, and build an empirical foundation on which more informed policy conversations can rest. The particular focus is AI deployment in the Global South, with an aim to better understand adoption challenges and how to address them. If the evidence base is thin for AI policy in general, then it is particularly so in countries across Africa, Asia, and Latin America, where data is scarce and narrative often outpaces analysis. Sometimes we are told that AI will leapfrog legacy infrastructure and accelerate development; at other times, that it will entrench dependency and widen the gap between digital haves and have-nots. One line of thought holds that the Global South is structurally disadvantaged and will fall further behind in the AI era; another insists that the region is leading in frugal innovation, building locally relevant tools, and pioneering use cases the Global North has barely begun to explore. Which of these narratives is true?

The answer is that both probably contain a measure of truth: The real picture, as always, is complicated and uneven. We need to see that picture clearly in order to understand the real trade-offs, how to prioritize resources and, more generally, how to design policy that enhances rather than diminishes the agency and well-being of AI users (and nonusers) across the world.

The evidence presented in this report is a first step in drawing that picture. It draws on three main sources of research, conducted over the first five months of 2026:��

1. Fourteen lengthy interviews and six structured surveys with AI practitioners, builders, and policymakers working on deployment across the Global South. These conversations focused on the day-to-day realities of building and sustaining AI deployments; they surfaced a number of obstacles and also identified glimmers of solutions that are emerging from these deployments.
2. A collection of 53 case studies of AI deployments, assembled from a mix of published reports, practitioner documentation, and some unpublished fieldwork. The corpus was used to elicit specific anecdotes and examples, and it was also analyzed in the aggregate to identify cross-cutting patterns in adoption barriers. The results, summarized in Table 1, do not always match what we found in the interviews (compute, for instance, surfaces less often in these case studies than in our interviews) but they provide a high-level overview of recurring challenges. Methodology for this aggregate analysis, as well as the case studies analyzed, is described in the Appendix.��
3. Finally, desk research, drawn from the growing literature on AI in developing contexts, was used to situate and contextualize the primary evidence.

Several caveats are worth stating up-front. The evidence base is not geographically or sectorally representative: It skews toward countries where the ecosystem is more active and the institutional framework is more robust (India, Brazil, and East Africa in particular). It also may overrepresent sectors such as agriculture, health, and low-resource language development, where the number of deployments has been greatest. Finally, it is biased toward organizations that have survived long enough to be written about; the failures that never made it to publication are almost by definition underrepresented.

This report is a starting point—an effort to build a more grounded picture of a landscape that has been described too often in the abstract. We hope to build on this work in the future with additional interviews and case studies. In the conclusion, we return to some open questions and directions for further research that would help strengthen the evidence base.

Table 1 | AI Deployment Case Study Analysis: Major Challenges Faced��

Note: Percentages reflect the share of case studies that describe each challenge as a substantive obstacle. One case study can be coded for multiple challenges.

This report is conceived as a landscaping exercise. Its primary intent is to map the terrain of AI adoption in the Global South and to surface challenges and workarounds. As such, it does not advance a single thesis. Instead, we highlight four cross-cutting observations that run through the empirical material. More orienting claims than arguments, they recur throughout the evidence and provide conceptual underpinnings for the following sections. They also provide scaffolding for our observations in the conclusion.��

First, AI adoption is best understood not as a problem of model or other technical performance but as a problem of alignment with real-world systems: infrastructure, data, institutions, society, and political economy. The frontier that matters for development outcomes in the Global South is not model capability, it is contextual fit.

Second, both the problems and the solutions we highlight are whole-of-system. Adoption challenges cannot be resolved at the technical layer alone; they require attention to the institutional and social environments as well. At the same time, technology continues to play a central role, though often with a distinctive flavor. Repeatedly, we found evidence of experimentation with locally relevant data, smaller and fine-tuned models, edge deployment, offline-capable systems, and other approaches and architectures that are emerging from or being refined in a part of the world that has long been confined to the periphery of technical innovation.

Third, constraint is generative as well as limiting. AI adoption in the Global South is everywhere shaped, and often hemmed in, by resource constraints. But we found that constraints are not only barriers, they also drive distinctive forms of innovation. Ultimately, we believe that some of these resource-constrained innovations may have relevance well beyond the Global South.��

Fourth, barriers to AI adoption are in most cases compounded for already excluded populations. Women, rural and remote communities, the less educated, and other historically marginalized groups face the adoption gap in sharper form than others. Evidence of gender disparities in particular surfaced repeatedly in our research. This calls for an approach to AI deployment and adoption that moves beyond generic “user” categories and is more intentional about the populations being served and how they are reached.