India vs Pakistan: AI Job Risk Compared (2026)

Two South Asian giants, one structural paradox

India and Pakistan are neighbours with intertwined histories and comparable labour market structures. Both economies rely heavily on agriculture, both have large informal sectors, and both have fast-growing but still small formal white-collar workforces relative to total employment. The ILO ILOSTAT 2025 Labour Force Survey data shows India employing 476.6 million workers and Pakistan employing 77.6 million - a 6x size difference that tracks roughly with their population ratio of 1.4 billion versus 231 million.

What is counterintuitive is the AI exposure score at the top of each labour market. Pakistan's clerical support workers score 8.5/10 - the maximum in this analysis - while India's clerical workers score 8.0/10. That is unusual. In most comparisons, the richer country has higher-scoring clerical workers because wealthier economies tend to concentrate more complex, information-heavy administrative work in that group. Here, the pattern reverses. Pakistan's clerical workforce, though smaller, carries tasks that AI can more directly automate than India's more varied clerical group.

The deeper story is about the economic deployment gap. AI exposure scores measure what AI can do to a job - not how soon employers will actually deploy it. At GDP per capita levels below $3,000, the business case for replacing low-wage clerks with AI tools is weak. For both countries, the headline risk scores are real but the practical timeline is long.

Side-by-side: all occupation groups compared

The table below shows every ISCO-08 major occupation group for both countries using ILO ILOSTAT 2025 Labour Force Survey data. India wage data is from ILO ILOSTAT 2025. Pakistan wage data at occupation-group level is not available in the ILO dataset for 2025.

Source: ILO ILOSTAT (CC BY 4.0), 2025 Labour Force Survey. AI scores shown as India / Pakistan where they differ for the same group; identical scores shown once. Pakistan wage data not available in ILO dataset at occupation-group level for 2025.

Why Pakistan's clerks score higher than India's

The 8.5 versus 8.0 clerical score difference is the central puzzle of this comparison. India's clerical workforce of 11.1 million is significantly larger than Pakistan's approximately 2.4 million clerks, yet India's group scores slightly lower. This reflects compositional differences within the clerical category - Pakistan's clerical workers are more concentrated in the most automatable sub-roles (data entry, records processing, basic correspondence) relative to India's more varied administrative workforce, which includes a larger share of workers in coordination and supervisory roles that involve more human judgment.

India's IT sector has also created a large class of workers who sit near but not quite within the clerical category - software developers and business process outsourcing roles who are classified as professionals (6.5/10) rather than clerical. This structural difference pulls India's clerical score slightly down from the maximum. Pakistan's equivalent formal economy workers are fewer and more routinely administrative in nature.

That said, the practical difference between 8.0 and 8.5 is narrow. Both clerical workforces face the same fundamental AI threat from language model tools that can draft documents, process data, and handle routine correspondence. The score difference is a compositional artefact, not a meaningful divide in actual risk.

The agricultural buffer: both countries protected by the same structural fact

The most important structural similarity between India and Pakistan is the dominance of agricultural and elementary work in their total employment. India has 161.9 million skilled agricultural workers (3.0/10) and 103.0 million elementary workers (2.0/10) - together representing over half of the entire Indian workforce of 476.6 million. Pakistan shows the same pattern: approximately 18.5 million agricultural workers and 18.6 million elementary workers out of 77.6 million total, together accounting for nearly half of Pakistan's employed population.

This agricultural buffer is the single most important factor suppressing average AI exposure in both countries. It is not a feature of a developed economy - it reflects the fact that the majority of employment in both countries is still in physical, location-dependent, variable-environment work that AI cannot currently perform. Weeding fields, harvesting crops, manual construction, domestic labour: these tasks require human bodies in specific places, responding to conditions that change minute to minute. AI scores these at 2.0 to 3.0 out of 10 for good reason.

The full India analysis covers how India's average AI exposure of approximately 3.5/10 is held down by this agricultural majority. Pakistan's average of 3.31/10 reflects an almost identical dynamic. A country's headline average score is largely a function of how much of its workforce remains in physically-grounded work - and by that measure, both India and Pakistan are structurally protected in 2026.

The wage economics of AI deployment

Understanding why AI disruption is distant in both countries requires understanding the economics of deployment. AI tools - whether software-as-a-service productivity tools, automation platforms, or enterprise AI systems - carry fixed costs. Those costs need to pay back against the wage savings they generate. At GDP per capita of $2,702 (India, World Bank 2025) and $1,595.91 (Pakistan, World Bank 2025), the wage savings from replacing a clerical worker are small relative to the infrastructure and licensing cost of deploying AI systems at scale.

Compare this to the US ($80,000+ GDP per capita), Germany, or Singapore, where a single clerical worker's annual salary can be $40,000 to $60,000. Automating that role with AI tools that cost $5,000 to $15,000 per year makes immediate business sense. In India or Pakistan, where the same clerical role pays $3,000 to $5,000 per year, the return on automation investment is minimal. Employers in both countries are far more likely to hire additional low-wage clerks than to invest in AI infrastructure.

This is the economic deployment gap that the UNDP Human Development Report 2025 (2023 data) also reflects: India's HDI of 0.685 (rank 134) and Pakistan's HDI of 0.544 (rank approximately 161) indicate lower levels of digital infrastructure, institutional capacity, and capital availability that further slow AI adoption timelines beyond the pure wage economics. Explore the live data for India and Pakistan in the interactive tool.

What this means for workers in both countries

For clerical workers in India and Pakistan, the AI threat is real in structural terms but distant in practical terms. The 10-plus year timeline reflects the compound effect of low wages, limited enterprise AI budgets, slower digital infrastructure rollout, and the scale of informal employment that sits outside formal automation entirely. Workers in both countries have time to develop adjacent skills - not just to avoid automation, but to participate in the productivity gains AI will eventually enable.

For India's 27.9 million professionals - the IT services and business process outsourcing workers who form the backbone of India's middle-class economy - the timeline is shorter. These workers earn enough that AI tools targeted at their roles (coding assistants, document AI, analytical automation) are already cost-effective for the multinational employers who offshore work to India. The risk for this group is not that an Indian employer replaces them directly with AI, but that the global demand for offshore IT services declines as AI tools reduce the need for human labour in those tasks altogether. This is the more urgent medium-term risk for Indian knowledge workers.

Pakistan's professional class of approximately 5.3 million is smaller and less globally integrated than India's. The immediate offshore-service risk is lower. But domestic digitisation of Pakistan's banking, telecommunications, and government sectors - actively underway - is already reducing demand for junior clerical roles in Karachi and Lahore. These workers face a more locally-driven version of the same risk: not sudden displacement, but slow erosion of hiring volume as organisations digitise.

See how India and Pakistan compare to the broader global picture in the US analysis (155.5 million workers), the UK analysis (34 million workers), and the India vs China comparison.

Economy context: India and Pakistan side by side

Economic context determines how fast and how deeply AI disruption moves through a labour market. The indicators below show the structural gap between the two countries - and explain why both remain in the distant-disruption category despite different peak AI scores.