Last updated: June 2026
Country | Rank |
|---|---|
India | 1 |
Brazil | 2 |
Malaysia | 3 |
Hungary | 4 |
Philippines | 5 |
Country | Rank |
|---|---|
Chile | 6 |
Czechia | 7 |
South Africa | 8 |
Bulgaria | 9 |
Argentina | 10 |
Country | Rank |
|---|---|
Poland | 11 |
Indonesia | 12 |
Romania | 13 |
Dominican Republic | 14 |
Peru | 15 |
Country | Rank |
|---|---|
Egypt | 16 |
Nigeria | 17 |
Kenya | 18 |
Morocco | 19 |
Ghana | 20 |
Country | Rank |
|---|---|
Pakistan | 21 |
Nepal | 22 |
Bangladesh | 23 |
Uganda | 24 |
Ethiopia | 25 |
Need a specific country comparison or regional breakdown for your story? Email press [at] ataraxismgmt.com and we’ll respond within 48 hours.
Country | Rank |
|---|---|
United States | 1 |
United Kingdom | 2 |
France | 3 |
Germany | 4 |
Australia | 5 |
Canada | 6 |
Japan | 7 |
The Global Outsourcing AI Readiness Index evaluates countries across four key factors influencing the future of outsourced work:
Each country receives a score from 0–100 for every factor, weighted according to its contribution to AI-enabled work readiness.
For example, a country with strong enterprise adoption but weak workforce literacy may have advanced technology deployments without sufficient talent capable of maximizing AI productivity gains. Conversely, countries with highly AI-literate populations but limited enterprise adoption may struggle to convert skills into economic value.
Scores are designed to be comparative and neutral. The index does not attempt to predict which countries will dominate AI permanently. Instead, it provides a snapshot of where nations stand today as organizations seek global talent capable of thriving in an AI-augmented environment.
Scores are based on publicly available datasets and expert interpretation, drawing from sources such as Microsoft, Cloudflare Radar, OpenAI, OECD, LinkedIn, Coursera Enterprise, and GitHub
The offshore outsourcing industry employs tens of millions of people. The Philippines alone has more than 1.7 million workers in IT-BPO. India’s technology services sector employs over 5 million. For both countries, and for dozens more, outsourcing is not an economic footnote, it is a foundational source of foreign exchange, employment, and middle-class formation.
The question being asked in every boardroom, government ministry, and trade publication that covers this industry is the same: is AI going to displace it? This index does not answer that question with a prediction. It answers it with data. Specifically, it measures how prepared each outsourcing destination is to compete in a market where the organizations sending work offshore are themselves rapidly adopting AI.
This index does not predict whether AI will replace offshore workers. It does not assess individual worker productivity, small business competitiveness, or the near-term displacement probability of any specific job category. What it measures is national-level AI readiness for large-scale outsourcing industry positioning.
This index should not be used as the sole decision-making factor for highly regulated, security-sensitive, or niche technical roles without additional evaluation.
SubIndex | Dimensions | What it Measures |
|---|---|---|
Population-Level AI Adoption (30%) |
| Share of working-age population actively using generative AI tools, with momentum signals and connectivity as enabling conditions |
Workforce AI Literacy (30%) |
| Professional depth of AI skill in the workforce: LinkedIn talent density, GitHub developer growth, median age and brain drain ratios |
Enterprise AI Adoption (25%) |
| Whether firms have moved beyond pilots into production AI workflows. The dimension most directly measuring outsourcing delivery quality |
Education Pipeline (15%) |
| Whether institutions are producing the next generation of AI-ready workers — the forward-looking dimension that predicts 5–10 year competitiveness |
All four sub-indices were tested across six weight variation scenarios (±5 percentage points per dimension). Equal weighting produces fewer rank crossings than any alternative tested, confirming it as the most stable and defensible structure.
The Global Outsourcing AI Readiness Index scores 25 Leading Outsourcing Destinations and 7 Consumer Markets across four sub-indices. Each sub-index measures a distinct layer of AI readiness.
Each sub-index scores countries on a scale of 0 to 100. Higher scores indicate greater AI readiness on that dimension. The four sub-index scores are averaged to produce the composite.
All scoring is relative to the 25-country provider peer group. A score of 80 does not mean a country has achieved 80% of some absolute ceiling. It means it is performing near the top of this specific peer group on that dimension.
This measures how broadly AI tools have penetrated daily life among the working-age population. It covers three things: how many people are currently using AI, how fast that number is growing, and whether the underlying infrastructure (internet access, electricity, smartphones) exists to support continued growth. Countries where AI is already a daily habit produce workforces that adapt to AI-augmented professional environments faster and with less friction.
This measures the professional depth of AI skill in the working population. It covers the concentration of AI-skilled professionals relative to the workforce, the size and growth trajectory of the developer community, and the demographic profile of the working-age population including median age and the degree to which skilled AI workers are leaving the country rather than staying. This sub-index answers the question a consumer market actually cares about: does this country’s workforce have the professional AI capability to deliver what we need?
This measures the extent to which organizations have integrated AI into their operations, rather than focusing on individual use. A workforce can be AI-literate while the companies employing that workforce still run entirely manual processes. This sub-index assesses the degree to which firms have moved beyond pilots and experiments into production AI workflows, the breadth of AI deployment across sectors, and the structural barriers, including data governance, skills gaps, and regulatory compliance costs, that are preventing organizations from scaling further.
This measures whether a country’s institutions are producing the next generation of AI-capable workers. Current workforce quality matters for today’s contracts. Pipeline quality determines whether that workforce can be sustained and grown. It covers the country’s position in global skills proficiency rankings, the pace at which professionals are enrolling in AI and generative AI training programs, the share of university graduates receiving STEM degrees, and the maturity of national policy for integrating AI into education curricula.
Microsoft’s report measures how many people in a country are actively using AI tools. It is the most precise population-level AI usage dataset available.
The difference is that Microsoft only answers one question: are people using AI? It does not measure whether companies have deployed AI into their operations, whether workers have professional AI skills, or whether schools are producing AI-ready graduates.
IBM’s index measures how many large companies in select countries have actively deployed AI into their business operations. It is the most reliable cross-country enterprise deployment dataset available.
The difference is coverage and scope. IBM surveys approximately 15 countries. It does not measure how individuals use AI, how skilled the workforce is, or how education systems are performing.
Coursera’s report ranks countries on skills proficiency and AI learning momentum based on activity across its platform. It is a strong signal of how actively a country’s professionals are upskilling in AI.
The difference is that Coursera only sees learners who have chosen to use its platform. Countries with strong traditional education systems but lower Coursera usage may look weaker than they actually are.
Cloudflare Radar measures internet traffic patterns, specifically which AI services are receiving traffic, how much of that traffic comes from real users versus automated bots, and how websites are managing AI crawler access. It reflects what is happening at the infrastructure layer of the internet.
The difference is that Cloudflare is an infrastructure measurement tool, not a readiness index. It does not produce country scores, does not measure workforce skills or enterprise deployment, and does not assess education systems.
LinkedIn’s index measures how concentrated AI skills are within each country’s professional workforce, based on what members list on their profiles. It is the most detailed cross-country picture of AI talent density available.
The difference is that LinkedIn’s reach is uneven. Countries where fewer professionals maintain LinkedIn profiles can appear to have less AI talent than they actually do. LinkedIn also measures self-reported skills rather than verified ability.
GitHub’s Octoverse tracks how developer communities are growing globally, which countries are adding the most developers, how open-source contribution is spreading, and how AI coding tools are being adopted by programmers. It reflects the health and growth of the technical builder community in each country.
The difference is that GitHub measures one specific population: software developers who use GitHub. It does not capture AI adoption by the general workforce, enterprise AI deployment, or whether education systems are producing graduates in relevant fields.
OpenAI’s report documents how its own products (ChatGPT and the OpenAI API) are being adopted by businesses globally. It covers which industries are growing fastest, how employees are using the tools, and which markets are expanding most rapidly.
The difference is that OpenAI’s report reflects adoption of one company’s products. A country where businesses are not OpenAI customers may still have high enterprise AI adoption through other platforms.
The OECD’s AI jobs and skills platform, built with LinkedIn, tracks how many job postings require AI skills and how AI talent concentration varies across countries and industries. It is a labor market signal showing where employers are actively demanding AI capability.
The difference is coverage. The OECD platform is strongest for high-income member countries and significantly thinner for lower-income provider nations.
This index deliberately does not answer that question with a prediction because the honest answer is that it depends on which workers, in which sectors, for which clients, and over what timeframe. What the data shows is that outsourcing destinations whose workforces are most actively integrating AI tools are not experiencing displacement. They are experiencing competitive differentiation.
The risk is not that AI replaces offshore workers wholesale. The risk is that outsourcing providers who do not build AI capability lose contracts to those who do. This index measures exactly that capability gap.
The Global Outsourcing AI Readiness Index is updated as meaningful new datasets become available.
Because many global AI datasets are published annually, scores may change as countries expand AI education, increase enterprise adoption, or improve workforce capabilities.
The index relies on publicly available datasets and internationally recognized sources covering AI adoption, workforce development, enterprise transformation, and education trends.
These sources may include Microsoft, Cloudflare Radar, OpenAI, OECD, LinkedIn, Coursera, GitHub, and national statistical agencies.
The Global Outsourcing AI Readiness Index is designed for:
Yes. This index is a comparative tool, not a verdict. A country may rank lower overall but still perform well for specific industries, language requirements, time-zone alignment, or cost profiles not captured in these four dimensions. The index highlights AI readiness.
High-income countries often demonstrate strong enterprise adoption and educational infrastructure.
However, the index evaluates overall outsourcing readiness rather than technological sophistication alone.
Countries with exceptional AI capability but limited workforce scalability, slower adoption among the broader population, or weaker outsourcing economics may rank differently.
No. All countries are evaluated using a standardized methodology and consistent weighting framework.
Differences in rankings reflect measurable variations in AI adoption, workforce preparedness, enterprise implementation, and educational development rather than geographic preference.
| Countries | Rank | Population adoption | Workforce AI literacy | Enterprise adoption | Education pipeline | TOTAL |
|---|---|---|---|---|---|---|
| India | 1 | 78 | 89 | 88 | 83 | 84.55 |
| Brazil | 2 | 79 | 76 | 77 | 69 | 76.1 |
| Malaysia | 3 | 78 | 68 | 77 | 84 | 75.65 |
| Hungary | 4 | 86 | 54 | 67 | 69 | 69.1 |
| Philippines | 5 | 69 | 76 | 71 | 52 | 69.05 |
| Chile | 6 | 78 | 52 | 74 | 70 | 68 |
| Czechia (Czech Republic) | 7 | 61 | 59 | 75 | 81 | 66.9 |
| South Africa | 8 | 78 | 63 | 65 | 53 | 66.5 |
| Bulgaria | 9 | 82 | 56 | 46 | 66 | 62.8 |
| Argentina | 10 | 74 | 57 | 59 | 57 | 62.6 |
| Poland | 11 | 57 | 70 | 48 | 74 | 61.2 |
| Indonesia | 12 | 57 | 59 | 60 | 75 | 61.05 |
| Romania | 13 | 69 | 63 | 42 | 62 | 59.4 |
| Dominican Republic | 14 | 77 | 45 | 46 | 45 | 54.85 |
| Peru | 15 | 58 | 44 | 41 | 63 | 50.3 |
| Egypt | 16 | 53 | 50 | 42 | 53 | 49.35 |
| Nigeria | 17 | 49 | 66 | 34 | 41 | 49.15 |
| Kenya | 18 | 47 | 59 | 35 | 47 | 47.6 |
| Morocco | 19 | 48 | 41 | 39 | 46 | 43.35 |
| Ghana | 20 | 43 | 54 | 31 | 36 | 42.25 |
| Pakistan | 21 | 42 | 55 | 25 | 35 | 40.6 |
| Nepal | 22 | 48 | 38 | 19 | 25 | 34.3 |
| Bangladesh | 23 | 36 | 37 | 25 | 31 | 32.8 |
| Uganda | 24 | 29 | 34 | 14 | 18 | 25.1 |
| Ethiopia | 25 | 27 | 33 | 14 | 18 | 24.2 |
| Countries | Rank | Population adoption | Workforce AI literacy | Enterprise adoption | Education pipeline | TOTAL |
|---|---|---|---|---|---|---|
| United States | 1 | 78 | 91 | 93 | 85 | 86.7 |
| United Kingdom | 2 | 90 | 79 | 84 | 76 | 83.1 |
| France | 3 | 96 | 68 | 71 | 82 | 79.25 |
| Germany | 4 | 76 | 76 | 80 | 89 | 78.95 |
| Australia | 5 | 87 | 72 | 80 | 74 | 78.8 |
| Canada | 6 | 81 | 72 | 71 | 83 | 76.1 |
| Japan | 7 | 66 | 62 | 79 | 77 | 69.7 |
How to Cite the Ataraxis Global Outsourcing AI Readiness Index (2026)
If you reference data from this index in articles, research, or presentations, please attribute it as:
Global Outsourcing AI Readiness Index
https://ataraxismgmt.com/global-outsourcing-ai-readiness-index
©️ Ataraxis Management, Inc.
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