Why Is AI Unevenly Distributed Across the World? Understanding the Global AI Adoption Gap
Artificial intelligence has long been presented as a technology capable of transforming the business world. Because many modern AI tools are inexpensive, easy to use, and require minimal technical infrastructure, it once seemed inevitable that they would spread quickly across the globe.
However, new research by Dafna Bearson (Harvard Business School) and Nataliya Wright (Columbia Business School) shows a different reality. AI adoption is highly unequal between countries. This global AI adoption gap is not caused by infrastructure, cost, or lack of technical skills.
Instead, it is shaped by which markets AI entrepreneurs choose to focus on.
Why Isn’t AI Spreading Equally?
Although most AI tools are easy to implement, only 14% of companies worldwide have adopted any AI technology. Adoption rates are much higher in English-speaking countries — especially the United States, United Kingdom, Canada, and Australia.
Meanwhile, companies in Asia, Latin America, and many parts of Europe lag behind.
The main reason: AI entrepreneurs develop products primarily for English-speaking markets.
Most AI founders:
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build their websites in English,
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design tools for English-speaking users,
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market their products to regions like the U.S. and U.K.,
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and follow investor pressure to “succeed in the U.S. first.”
Because of this narrow focus, companies in non-English-speaking markets do not encounter many AI tools at all. The researchers call this lack of visibility “search friction.”
These companies are not avoiding AI because they lack capability. They avoid it because no one has targeted them.
How Was the Research Conducted?
To measure the global AI adoption gap, the researchers built a large dataset covering:
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88,000 business-to-consumer companies
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171 countries
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data from 2012 to 2023
Using BuiltWith, they identified which AI tools companies used — such as analytics platforms, chatbots, and recommendation engines. They also analyzed AI startup websites to determine which languages their products targeted.
The findings were clear:
More than 80% of AI entrepreneurs use English-language tools, and their products are adopted mostly in English-speaking markets.
Interviews with AI founders also revealed strong incentives:
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access to larger customers,
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more valuable data,
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stronger investor support,
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faster growth opportunities.
As a result, non-English-speaking regions rarely become early target markets.
What Did the Study Reveal About the Global AI Adoption Gap?
1. English-speaking companies are 12% more likely to adopt AI.
2. The adoption gap cannot be explained by infrastructure, education, or economic conditions.
Even when technical environments are similar, adoption levels remain unequal.
3. The issue is not technical “fit.” The real issue is visibility.
Companies in many regions simply never see these tools.
The researchers also tested whether AI is less effective in non-English-speaking countries.
Surprisingly, they found the opposite:
Companies in non-targeted markets often show even stronger performance gains after adopting AI — in revenue, efficiency, and overall growth.
This contradicts the assumption that “AI doesn’t work in emerging markets.”
Why Does This Gap Matter?
For policymakers:
AI does not spread naturally across borders. Governments can use incentives, grants, and localization programs to reduce the global AI adoption gap.
For entrepreneurs:
Non-English-speaking markets represent a major untapped opportunity with less competition and significant growth potential.
For companies in under-targeted regions:
If you do not see many AI tools, the problem is not your readiness.
You simply may not have been targeted.
Conclusion
The research demonstrates that global AI inequality does not stem from technical limitations.
It comes from the strategic market choices of AI entrepreneurs.
In theory, AI is a global technology.
In practice, it reaches only specific countries — widening the global AI adoption gap.
Understanding this divide is essential for building an AI ecosystem that is truly global and inclusive.
Original Study
Bearson, D., & Wright, N. (2025). Strategic Targeting and Unequal Global Adoption of Artificial Intelligence. Columbia Business School.
Full paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5187851
