Global AI Adoption in 2025: Accelerating Use, Deepening Divides, and What the Data Means for Georgia

According to the 2026 report by Microsoft’s AI Economy Institute, which analyzes global trends in the second half of 2025, artificial intelligence has decisively moved beyond the experimental phase. Roughly one in six people worldwide now use generative AI tools for learning, work, or everyday problem-solving. In just six months, global adoption increased from 15.1 percent to 16.3 percent, an unusually fast pace for a technology still in its early diffusion stage. Yet behind this rapid expansion lies a more structural and less optimistic story: AI is spreading, but it is spreading unevenly.

At the global level, the most striking pattern is the widening gap between the Global North and the Global South. By the end of 2025, 24.7 percent of the working-age population in the Global North was using generative AI tools, compared to only 14.1 percent in the Global South. The gap between the two groups did not narrow as adoption increased; it grew. What was a 9.8-percentage-point difference earlier in the year expanded to 10.6 points within six months. This confirms a concern long raised by institutions such as the OECD and the World Bank: digital technologies can become amplifiers of inequality when adoption is driven primarily by income levels, infrastructure quality, language availability, and institutional capacity rather than by inclusive policy design.

The countries leading global AI adoption are not surprising, but their consistency is revealing. The United Arab Emirates, Singapore, Norway, Ireland, and France remain at the top of the rankings. By the end of 2025, nearly two-thirds of the working-age population in the UAE was actively using AI tools. This outcome is not the result of spontaneous market enthusiasm. It reflects long-term state planning, early investments in digital infrastructure, regulatory experimentation, and the systematic integration of AI into public services. In these countries, AI adoption is closely linked to trust, usability, and visibility in everyday interactions with the state and the economy.

At the same time, the data challenges a common assumption that technological leadership automatically translates into mass adoption. The United States remains a global leader in AI research, infrastructure, and frontier model development, yet only about 28 percent of its working-age population uses generative AI tools. In global rankings, this places the US well below smaller, more digitally coordinated economies. Similar patterns can be observed in other advanced economies such as Germany and Japan. Innovation capacity alone does not guarantee widespread use. Cost, integration into daily workflows, linguistic performance, regulatory clarity, and public trust all play a decisive role.

One of the most instructive cases in 2025 is South Korea. Over the course of a single half-year, the country jumped seven positions in global rankings, with AI usage rising from roughly 26 percent to over 30 percent of the population. This surge was not accidental. It resulted from a convergence of three factors: rapid institutionalization of national AI policy, major improvements in large language model performance in the Korean language, and the emergence of consumer-facing AI features that made the technology immediately useful for ordinary users. AI became embedded in schools, workplaces, and public services, effectively turning it into part of everyday digital infrastructure rather than a specialized tool. For countries with smaller or less widely supported languages, this experience carries an important lesson: linguistic quality is not a secondary issue but a core driver of adoption.

Another defining feature of the 2025 landscape is the rise of open and free AI models. Platforms such as China’s DeepSeek gained significant traction in regions where Western AI services are expensive, restricted, or poorly localized. In parts of Africa and several post-Soviet and sanctioned economies, usage of such platforms significantly outpaced that of paid Western alternatives. This trend underlines a critical point: accessibility and affordability often matter more for diffusion than marginal differences in model performance. Analysts from Bloomberg and Carnegie Endowment have already noted that open-source AI is increasingly intertwined with geopolitical influence, particularly in regions historically excluded from early waves of technological adoption.

Against this global backdrop, Georgia occupies a modest but meaningful position. In 2025, AI adoption in Georgia increased from approximately 17.3 percent to 18.2 percent of the working-age population. This confirms that generative AI is present and growing, but the pace of growth remains below the global average and far behind leading economies. Georgia performs better than several neighboring countries, yet it trails Central and Eastern European states where AI is being actively integrated into education systems, public administration, and small-business support structures.

Georgia’s case is especially important because it illustrates the difference between potential and diffusion. The country has a growing IT sector, relatively strong digital connectivity, and rising interest in generative AI tools. However, usage remains concentrated among specific groups: technology professionals, freelancers, students, and urban, highly educated users. This mirrors patterns identified in EU digital economy assessments for other small and emerging economies, where advanced technologies initially cluster in narrow segments of society unless deliberate efforts are made to broaden access.

What the global evidence from 2025 ultimately shows is a paradox. Artificial intelligence has enormous potential to boost productivity, support learning, and enable small economies to leapfrog traditional development constraints. At the same time, without inclusive strategies, it risks reinforcing existing economic, linguistic, and institutional divides. For Georgia, the central question is no longer whether AI will become part of everyday life. It already is. The real question is whether it will remain a tool used by a relatively small, digitally privileged group, or whether it will be transformed into a broad-based driver of economic modernization and social inclusion. The answer will depend less on the technology itself and far more on policy choices, education systems, and the speed with which AI is translated into tangible public value.