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AI could lift productivity and support economic growth and diversification across Africa, the Middle East, and Türkiye—but the gains are neither automatic nor evenly distributed. They depend on whether economies can adopt and adapt AI, and whether businesses and workers can access reliable electricity, digital infrastructure, computing power, relevant data, skills, and capable institutions. The available estimates are scenarios and regional analyses, not comparable forecasts for every country.
What determines AI’s economic value?
AI’s value is not measured simply by how many people use a chatbot or how much technology a country imports. The economic question is whether organizations can apply AI to useful tasks, improve how work is done, and spread those improvements across firms and sectors.
The International Monetary Fund (IMF) frames the potential impact in terms of exposure to AI, preparedness, and access to data and technology. Those factors help explain why the same technology could produce different outcomes in different places. A sector may have tasks that AI can assist with, for example, but see little benefit if businesses lack dependable power, connectivity, skilled workers, or a way to use relevant data.
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What do the estimates actually say?
The figures below describe different kinds of evidence. The Sub-Saharan Africa figures are conditional scenarios from the IMF’s 2026 analysis; they are not observed gains or guaranteed forecasts. The global comparison is a modeled estimate, while Türkiye’s statistics describe productivity and exports—not AI’s measured contribution.
| Measure | Figure | What it does—and does not—show |
|---|---|---|
| Potential productivity increase in Sub-Saharan Africa | 0.2%–2.1% over the next decade | An IMF (2026) scenario range conditional on policy choices and AI’s eventual economic effect; not a result already achieved or an estimate for all African countries. |
| Possible Sub-Saharan Africa growth contribution at the upper potential | Nearly 0.5 percentage point of annual GDP growth over the same decade, or about 4% cumulatively | An IMF (2026) modeled possibility, not an unconditional forecast for every country in the region. |
| Estimated growth impact in advanced economies compared with low-income countries | More than double | The IMF’s 2025 global model comparison; it is not a regional estimate for Africa or the Middle East. |
| Türkiye labor productivity growth | 3.1% annually, compared with 0.9% across OECD regions, 2012–2022 | OECD (2024) productivity context, not a measurement of growth caused by AI. |
| Türkiye’s share of medium-high technology exports | 32.2% in 2015; 37.5% in 2024 | OECD (2025) evidence of industrial context, not proof of AI adoption or its effects. |
These numbers should not be collapsed into one forecast for the three regions. They cover different geographies, time periods, and measures, and only the Sub-Saharan Africa estimates directly model possible AI-related economic effects in a part of the regions covered by the headline.
How can AI boost productivity in Africa?
The IMF’s 2026 analysis focuses on Sub-Saharan Africa, not the whole African continent. It describes AI as a general-purpose technology with potential to reshape productivity, labor markets, and growth. The central challenge is whether countries can adopt, adapt, and scale it quickly enough to capture benefits.
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Start with conditions that make adoption possible
The IMF identifies unreliable or insufficient electricity, limited digital infrastructure, scarce technical skills, and gaps in regulatory and institutional capacity as constraints in the region. These are practical bottlenecks: without reliable power and connectivity, organizations cannot dependably run or access AI systems; without skills and suitable rules, they may struggle to select, adapt, and deploy them responsibly.
- Connectivity: dependable digital networks let people and organizations reach online tools and services.
- Compute: computing resources are needed to develop, train, or run AI systems, whether locally or through cloud services.
- Context: data that reflects local languages, needs, and circumstances can make systems more useful to the people expected to use them.
- Competency: technical and workplace skills help people apply AI and assess its outputs.
- Institutional capacity: effective institutions and regulatory frameworks help support adoption and manage risks.
These foundations are complementary. Improving only one may not be enough if another remains a binding constraint. The IMF’s scenario range therefore illustrates potential under differing conditions; it does not establish that the modeled gains will occur automatically.
How can Middle Eastern economies benefit from AI?
The IMF’s 2026 regional analysis covers the Middle East, North Africa, Afghanistan, and Pakistan (MENAP), as well as the Caucasus and Central Asia. That is broader than the Middle East alone, so its findings should not be presented as a forecast for every Middle Eastern country.
Across that wider grouping, the IMF identifies potential for AI to raise productivity, strengthen medium-term growth, and support economic diversification. It emphasizes that outcomes will depend on exposure to AI, preparedness, and access to advanced technologies. Economies with stronger infrastructure, skills, and institutional capacity can focus on developing innovation ecosystems and adapting legal and regulatory frameworks. Economies less prepared to adopt AI need to strengthen basic digital infrastructure and human capital.
The available analysis supports this broad distinction in priorities, not a country-by-country ranking or a single numerical estimate for the Middle East. The sources do not establish comparable, current country-level estimates of AI adoption and economic effects across the Middle East and North Africa.
What makes Türkiye’s case different?
Türkiye’s evidence is useful context for its productivity and technology agenda, but it does not measure AI’s effect on the economy. The OECD’s 2025 Economic Survey says future growth will depend more on productivity, which has slowed and remains below the OECD average. It sets out a three-part strategy: encourage innovation and adoption of new technologies, improve workforce skills, and reduce barriers to business dynamism, including through trade openness.
The increase in medium-high technology exports—from 32.2% of exports in 2015 to 37.5% in 2024—signals industrial upgrading, not evidence that AI caused the change or is already widely adopted.
Productivity also varies within Türkiye. OECD figures for 2012–2022 show annual labor productivity growth of 4.7% in Southern Aegean and Western Black Sea – West, compared with -0.1% in Southeastern Anatolia – Middle. These local differences matter when considering where the capacity to adopt new technologies may be strongest, but the figures are productivity measures, not AI-impact estimates.
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Countries, firms, workers, and regions do not begin with equal access to infrastructure, technology, data, or skills. The IMF’s 2025 global model estimates that AI’s growth impact in advanced economies could be more than double the impact in low-income countries. That is a modeled global comparison—not a specific estimate for any country in Africa or the Middle East—but it highlights the risk that better-prepared economies capture more of the upside.
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Access and preparedness can influence that gap. If computing resources, relevant data, and skills are scarce or concentrated, AI may be harder for smaller firms and less-connected communities to use. Improving the foundations for adoption can help, although the sources do not establish that doing so will eliminate differences in outcomes.
What adoption statistics can—and cannot—tell us
The World Bank reported that ChatGPT was used in April 2025 by 5.8% of internet users in upper-middle-income countries, 4.7% in lower-middle-income countries, and 0.7% in low-income countries. These are World Bank income-group figures, not adoption rates for Africa, the Middle East, or Türkiye. They measure use of one named service, not the overall adoption of AI across businesses, public services, or the economy.
For that reason, these figures cannot substitute for comparable country-level evidence on AI’s economic effects. A single service’s use does not show whether organizations are using AI productively, whether the benefits are spreading, or how much growth it has caused.
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How to judge claims about AI’s value
- Check the geography: Sub-Saharan Africa is not all of Africa, and the IMF’s Middle East and Central Asia grouping extends beyond the Middle East.
- Check the measure: productivity, GDP growth, technology exports, and use of a particular AI service are different indicators.
- Check the evidence type: a modeled scenario is a possible outcome under stated conditions, not an observed result or guaranteed forecast.
- Check the comparison: income-group usage figures and global model results should not be relabeled as regional or country-specific findings.
- Check who can participate: electricity, connectivity, compute, data, skills, and institutional capacity all affect whether potential gains can be realized.
The evidence points to substantial but conditional potential. For policymakers and organizations, the most useful question is not simply how much AI might add, but which foundations and capabilities need to improve for people and businesses to turn that potential into productivity—and who will be able to share in the gains.
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