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In late 2018, researchers from Google Health partnered with Thailand’s Ministry of Public Health to deploy an in 11 rural clinics. The tool was designed to detect diabetic retinopathy—a leading cause of blindness—from retinal scans, addressing a serious shortfall in the country’s national screening program. Thailand has roughly 4.5 million people with diabetes but only around 200 retinal specialists. In controlled laboratory settings, the algorithm had achieved more than 90 percent accuracy and performed at a level comparable to human medical specialists. The deployment promised to replace days of specialist review with instant, algorithmic triage and to improve care and outcomes for diabetes patients.

The tool was deployed across 11 clinics and 7,600 patients—results were not as expected. The model had been trained on high-quality retinal images taken in well-lit lab conditions; in actual clinics, where lighting was variable and rooms were sometimes shared with other procedures, more than a fifth of the images nurses captured were rejected as too low-quality to process. Patients whose images were rejected—not necessarily an indication of disease—were referred to specialists hours away, requiring them to take time off work and imposing psychological hardship. Slow internet connections delayed uploads, adding to wait times and frustration. Overall, as a follow-up , a tool designed to accelerate care—and proven to do so in the lab—had instead introduced new bottlenecks when released into the world.

The underwhelming deployment in Thailand is hardly an isolated case. Since the release of ChatGPT in late 2022, as frontier AI labs have competed relentlessly to announce breakthrough models with ever-more sophisticated capabilities, another challenge, more mundane yet arguably more important, has hung over the field. The most pressing task today is not building more capable models but rather deploying and fostering the real-world adoption of models that already exist. Much of the AI conversation, focused on leaderboard performance and swelling parameter counts, overlooks this challenge. Yet many of the most important questions in the field—including the sustainability of the AI investment boom and the likelihood of promised productivity gains—hinge on bridging this “,” or what some have termed a “.” Figure 1, from a recent , illustrates the point: A striking gap remains between what frontier models can in theory do and what they are actually doing today.¹

Figure 1 | Theoretical Capability and Observed Usage by Occupational Category

The conversation about AI’s adoption gap has thus far been largely focused on the Global North—notably in pointing to lagging or uneven workplace usage in the United States. As the Thailand example suggests, though, the underlying dynamic is global. In fact, the challenge may be particularly acute—and, as this report will argue, carries several distinctive dimensions—in the Global South, where AI is being deployed in resource-constrained environments amid sometimes limited state capacity and fragile institutions. According to Microsoft’s , aptly subtitled “A Widening Digital Divide,” roughly 24.7 percent of the working-age population in the Global North now uses generative AI tools, compared with 14.1 percent in the Global South. That gap is growing, with adoption in the Global North expanding nearly twice as fast as in the Global South during the second half of 2025. The risks of this trajectory are evident. If AI delivers on even a fraction of its promised gains, uneven adoption will translate directly into uneven development—setting in motion what the United Nations Development Programme (borrowing from economic historian ) calls “.”

This report sets out to better understand adoption challenges in the Global South: their nature, their drivers, and—especially important—the solutions and workarounds already emerging on the ground. The report’s distinguishing feature is empirical. While some of the challenges and remedies discussed here are known at a high level and have been the subject of valuable prior work, this report builds its case from the ground up, through structured interviews with AI practitioners and a compendium of 53 adoption case studies. This approach allows for a more granular and nuanced account than is typical in the policy literature: concrete characterization of specific bottlenecks; attention to the textures of particular contexts and sectors; and close description of the improvisations that practitioners are deploying, often without recognition or support. One of the premises of this report is that such insights, particular and granular though they may be, can inform broader guiding principles and solutions, and potentially articulate a model of AI development for the Global South that is distinct from the dominant paradigms currently being advanced by the United States and China.

Section I provides methodological background, as well as some cross-cutting observations and recurring themes that run through the empirical evidence. The evidence itself is divided into a technical section (II) and a nontechnical one (III); at times the distinction blurs, but the intent is to highlight the fact that AI adoption is an all-of-society exercise, not just a matter of successful technical deployment. In both empirical sections, we discuss a number of parameters that our research suggests play a significant role in AI adoption. For each parameter, we discuss both how it is currently limiting AI adoption and the responses that on-the-ground practitioners are already developing. Together, these challenges and responses provide the basis for Section IV, the conclusion, which tries to identify open questions and directions for further research.