India vs China: Which Workforce Faces Greater AI Risk in 2026?

Two giants, very different AI profiles

India and China are the two largest workforces tracked in the ILO ILOSTAT 2025 Labour Force Survey data. Together they account for roughly 838 million employed people. The comparison is natural - but the structural differences between the two labour markets are more significant than the headline AI scores suggest.

Both countries have clerical workers who score 8.5/10 on AI exposure, the highest possible in this analysis. That is where the similarity ends. India's economy is still majority agricultural and informal. China's economy is more industrialised, more urbanised, and carries a much larger share of workers in the professional and administrative roles where AI does its most direct damage.

The result is a tale of two very different AI disruption profiles. India's risk is concentrated and numerically smaller. China's risk is broader and involves far more workers in higher-wage, higher-productivity roles that AI tools can directly reach.

Side-by-side: all occupation groups compared

The table below shows every ISCO-08 occupation group for both countries side by side, using ILO ILOSTAT 2025 data. India wage data is from ILO 2025. China has no ILO wage data available at the occupation-group level for 2025.

Source: ILO ILOSTAT (CC BY 4.0), 2025 Labour Force Survey. China wage data not available in ILO dataset at occupation-group level. Dash (-) indicates group not separately reported in ILO data for that country.

Why India's clerical workers face a different kind of risk

India's 11.1 million clerical support workers score 8.5/10 on AI exposure - identical to China's clerks. But the context is different. India's clerical workforce earns an average of $3,339 per year, according to ILO ILOSTAT 2025 data. That is a very low wage by global standards, which creates a counterintuitive dynamic: automation of low-wage clerical work in India may be slower than in richer countries, simply because the cost savings are smaller relative to the cost of deploying AI infrastructure.

India's 27.9 million professionals - software engineers, analysts, lawyers, accountants - score 6.5/10. This is the group where India's exposure is most internationally significant. India's technology sector has built a major share of its global competitiveness on IT services and business process outsourcing. These are precisely the functions that AI tools now perform with increasing capability. The risk for Indian professionals is not just domestic job loss - it is also erosion of the offshore IT services market that underpins a large part of India's middle-class employment.

India's country data page shows the full occupation breakdown for all 476.6 million workers. The most important structural fact: 264.9 million workers - over half the Indian workforce - sit in the two lowest-scoring groups (agricultural and elementary occupations at 2.0-3.0/10). These workers face minimal direct AI exposure in 2026. Their risk is more about economic displacement pressure as higher-risk groups adapt, rather than direct AI job substitution.

China's manufacturing base: the robotics question

China's largest occupation group by worker count is craft and related trades workers - 93.6 million workers at 2.5/10 on AI exposure. For China, this group matters because it overlaps heavily with manufacturing. And manufacturing in China faces a different automation threat than the AI exposure score alone captures: robotics.

AI exposure scores measure the susceptibility of work tasks to AI systems - language models, image recognition, decision automation. They do not separately score industrial robotics, which is China's larger near-term automation threat in factories. The 50.0 million plant and machine operators (3.0/10 AI exposure) and 93.6 million craft workers (2.5/10) are somewhat protected from pure AI automation but face increasing pressure from industrial robots in manufacturing environments.

Where AI directly hits China is in its professional and clerical sectors. 81.8 million Chinese professionals at 6.5/10 - the largest professional workforce of any country in the ILO dataset - are in the direct path of AI tools that can write code, draft contracts, produce financial analyses, and handle complex communications. This is a far larger group than India's 27.9 million professionals. The absolute number of high-exposure workers in China is substantially higher than in India, even accounting for China's smaller total workforce.

Explore the full China workforce breakdown at the China country data page or the interactive explore tool.

The wage gap changes everything

One of the most important factors in how AI disruption plays out is wage level - and the gap between India and China on this dimension is stark. China's GDP per capita was $13,862 in 2025 (World Bank). India's was $2,702. That is a 5x gap.

Higher wages increase the economic incentive for employers to automate. A Chinese clerical worker - even without ILO wage data at occupation level - earns far more than an Indian clerical worker earning $3,339 per year. This means AI tools that cost the same to deploy in both countries deliver a much larger return on investment in China. The business case for automation comes faster, and the deployment timeline compresses.

For India's professionals, the wage gap also matters in a different direction. Indian software engineers and business analysts earn significantly less than their counterparts in the US, UK, or China. This has historically made India's IT sector globally competitive through cost arbitrage. As AI systems reduce the cost of producing IT services output, the wage advantage that India offers shrinks. An AI system does not need a salary at all. The medium-term risk for India's 27.9 million professionals is therefore not just domestic automation - it is the narrowing of the offshore cost advantage that supports their roles.

India's GNI per capita PPP was $8,475 in 2025 (UNDP HDR 2023/24, 2022 data), compared to China's $19,556. The HDI gap tells a similar story: India at 0.644 (rank 134) versus China at 0.788 (rank 75), per UNDP HDR 2023/24. Higher human development levels correlate with faster AI adoption capacity - better digital infrastructure, higher digital literacy, and stronger institutional frameworks for deploying new technology.

What this means for workers in both countries

For workers in India, the near-term risk is concentrated in two specific groups: clerical workers (11.1 million) and professionals - especially IT services and BPO roles (a subset of the 27.9 million professionals). Workers in agriculture and elementary occupations are not in the direct firing line of AI in 2026, though economic pressure from other sectors can ripple through to these groups over time.

For clerical workers in India, the realistic disruption timeline is 5 to 10 years - longer than in richer countries, because the cost economics of AI deployment are slower to close at lower wage levels. For IT professionals, the timeline is shorter - AI coding tools, document generation, and process automation are already affecting productivity benchmarks in the offshore services industry.

For workers in China, the picture is more urgent across a broader population. The 33.6 million clerical workers and 81.8 million professionals face AI exposure at a wage level where automation investments pay back quickly. Chinese employers in finance, technology, insurance, and professional services already have AI deployment budgets that put clerical and junior professional roles at risk within 3 to 7 years.

The safest path in both countries is identical to what the data shows globally: roles requiring physical presence in variable environments (craft trades at 2.5/10) and direct human care. In India specifically, the agricultural sector's 161.9 million workers remain largely insulated from AI displacement in 2026 - though the informal nature of this employment means any economic disruption in formal sectors can still affect livelihoods indirectly.

See how India and China compare to the broader picture in the US analysis (155.5 million workers) and the US vs World comparison.

Economy context: India and China side by side

Economic context is essential for understanding how fast and how deeply AI disruption moves through a labour market. Higher GDP, higher wages, and higher HDI all accelerate deployment timelines.