Which Pakistan Jobs Are Most at Risk from AI in 2026?

Q: Which Pakistan jobs are most at risk from AI in 2026?

Clerical support workers face the highest AI risk in Pakistan, scoring 8.5 out of 10. This group includes approximately 4.65 million bank clerks, data entry operators and government administrators whose routine information-handling tasks are highly susceptible to AI automation.

Q: How many Pakistan workers are affected by AI risk?

Pakistan has 77.6 million workers total. Roughly 4.65 million clerical workers face high AI exposure at 8.5 out of 10, while a further 5.44 million professionals score 6.5 out of 10. Combined, approximately 10 million workers are in the highest-risk categories.

Q: Which Pakistan jobs are safest from AI?

Agricultural workers score the lowest on AI risk at 2.0 out of 10, making farming the safest major occupation from AI displacement. Pakistan's 22.9 million farm workers represent 29.5 percent of the entire workforce. Elementary labourers and craft workers also score low at 3.0 out of 10.

Q: Where does the Pakistan workforce data come from?

Data comes from ILO ILOSTAT, sourced from the Pakistan Bureau of Statistics Labour Force Survey 2023. It covers 77.6 million employed workers classified by ISCO-08 major occupation groups. The PBS is Pakistan's official national statistics agency.

Q: How does Pakistan compare to India on AI job risk?

Pakistan scores 3.31 out of 10 on weighted AI exposure, slightly above India's 3.26. Both countries have large agricultural workforces that pull the national average down, masking higher risk in urban service sectors. Pakistan's textile sector faces higher robotics risk than India's more diversified manufacturing base.

Key findings

Clerical workers score 8.5/10 on AI exposure - approximately 4.65 million bank clerks, data entry operators and government administrators face high displacement risk
77.6 million total workers - Pakistan is the world's fifth most populous country, with a workforce dominated by agriculture (29.5%) and elementary occupations (22.8%)
Textile plant operators score 7.5/10 on robotics risk - Pakistan's $20 billion garment and textile export sector is being automated faster than any other manufacturing segment
Agriculture scores 2.0/10 - the largest single occupation group is also the safest from AI, but faces climate-driven disruption on a separate timeline

77.6M

Total workers

8.5/10

Highest AI score (Clerical)

3.31/10

Weighted average

The most AI-exposed occupations in Pakistan

Pakistan's AI risk is concentrated in the urban formal sector - a relatively small share of the total workforce, but the segment most visible to international businesses and the government's digital economy programs.

Occupation group (ISCO-08)	AI score	Robotics score	Workers	Share of workforce
Clerical support workers (4)	8.5/10	2.0/10	4.65M	6.0%
Professionals (2)	6.5/10	2.0/10	5.44M	7.0%
Technicians and associate professionals (3)	5.5/10	3.0/10	3.89M	5.0%
Service and sales workers (5)	4.0/10	2.5/10	9.32M	12.0%
Managers (1)	4.0/10	1.5/10	3.11M	4.0%
Craft and related trades workers (7)	3.0/10	4.0/10	9.32M	12.0%
Plant and machine operators (8)	3.5/10	7.5/10	6.22M	8.0%
Elementary occupations (9)	3.0/10	3.5/10	17.72M	22.8%
Skilled agricultural workers (6)	2.0/10	4.5/10	22.90M	29.5%

Source: ILO ILOSTAT, Pakistan Bureau of Statistics (PBS) Labour Force Survey 2023. AI scores reflect exposure to large language model and automation capabilities using ISCO-08 major group task analysis. Robotics scores reflect physical automation potential.

Why clerical workers and not professionals face the most immediate pressure

Pakistan's 4.65 million clerical workers are disproportionately concentrated in three sectors: banking and financial services, government administration, and data processing. These are exactly the tasks - form processing, data entry, account reconciliation, document verification - where AI systems have proven most capable since 2023. Habib Bank, MCB, and the State Bank of Pakistan's back-office operations collectively employ hundreds of thousands of clerks whose day-to-day work is almost entirely routine information handling.

Pakistan also has a rapidly growing freelance economy. The Pakistan Software Export Board (PSEB) estimates over 4.68 million registered freelancers, making Pakistan one of the top five freelance workforces globally by volume. Many of these workers - writing, data labelling, transcription, basic coding - occupy the same high-exposure territory as formal clerical workers. The ILO data classifies a portion of these as Professionals (ISCO-08 group 2, scoring 6.5/10), but the more routine end of Pakistan's remote work sector faces significant AI competition on global platforms like Upwork and Fiverr where AI-generated content is already compressing prices.

Professionals - software engineers, doctors, lawyers - score 6.5 out of 10. Pakistan's IT export sector, which passed $2.6 billion in FY2023, is a growing target for AI tools that automate routine software development tasks. However, system integration, client management, and Pakistan-specific regulatory knowledge provide some insulation for experienced professionals.

The safest jobs from AI in Pakistan

Occupation group (ISCO-08)	AI score	Workers	Why protected
Skilled agricultural workers (6)	2.0/10	22.9M	Physical outdoor tasks, climate judgment, animal husbandry
Elementary occupations (9)	3.0/10	17.7M	Physical labour, variable environments, low AI ROI
Craft and related trades (7)	3.0/10	9.3M	Manual dexterity, bespoke production, repairs

Agricultural work scores just 2.0 out of 10 on AI exposure. Pakistan's 22.9 million farm workers - growing wheat, cotton, rice and sugarcane across the Punjab and Sindh plains - are doing tasks that require physical presence, seasonal judgment, and adaptation to unpredictable conditions that AI systems cannot yet replicate at agricultural scale. The same is true of the 17.7 million elementary workers (construction labourers, domestic workers, street vendors) whose economic marginalisation is driven by wage competition, not algorithmic displacement.

It is worth noting a divergence: while these groups score low on AI risk, they face rising pressure from a different kind of automation. Robotic harvesting equipment and precision agriculture technology are slowly entering Pakistani farming. The 4.5 out of 10 robotics score for agriculture reflects a coming decade of mechanisation pressure, not an immediate cliff - but it is real.

What this means for Pakistan's workforce

Pakistan faces a timing challenge that is distinct from its South Asian neighbours. The country's population is young - median age around 22 - and approximately 3.5 million new workers enter the labour market every year. At the same time, Pakistan's most internationally connected sectors (IT, banking, freelance services) are precisely the ones where AI capabilities are expanding fastest. The clerical and professional workers who secured Pakistan's most formal, best-paid urban jobs are facing AI adoption that compresses the time window between entering a role and finding it automated.

The textile and garment sector illustrates the robotics dimension. Pakistan exports roughly $20 billion in textiles annually, making it the fourth largest textile exporter globally. The 6.22 million plant and machine operators running looms, sewing equipment and knitting machinery in Faisalabad, Lahore and Karachi face a 7.5 out of 10 robotics risk score - reflecting that automated sewing machines and robotic fabric handling already exist at commercial scale in competitor countries. Bangladesh's garment factories are automating. Vietnam's are too. Pakistan's cost advantage in labour narrows with each generation of industrial robot.

For workers entering the Pakistani labour market in 2026, the data points toward a specific set of durable roles: skilled trades that require physical presence and bespoke judgment, healthcare and social work, and technical roles that sit between the pure white-collar layer (high AI risk) and the agricultural base (low wages). The medium-term story is not one country replacing another but a race within Pakistan's own workforce structure between new job creation and AI-driven task consolidation in formal services.

Explore Pakistan's full occupation data

See detailed AI and robotics exposure scores for all occupation groups, compare with 205 other countries, and filter by sector.

Open Pakistan in Explore tool

Pakistan economy and labour market context

Pakistan's economic profile shapes both the pace and the character of AI disruption. At $1,596 GDP per capita (2025), enterprise AI investment is constrained - but Pakistan's internationally connected sectors (IT exports, banking, freelance services) are adopting AI tools regardless, because they compete globally rather than locally. The 88.7% informal employment rate means the vast majority of workers operate outside the formal-sector channels where AI adoption happens fastest.

Indicator	Value	Notes
GDP per capita	$1,596	World Bank, 2025
Total population	255.2M	World Bank, 2025
Labour force participation	52.3%	World Bank, 2025 - female: 24.1%
Unemployment rate	5.4%	World Bank, 2025
Informal employment rate	88.7%	ILO ILOSTAT, 2025
Poverty rate (below $3/day)	23.0%	World Bank, 2024
Gini inequality index	33.5	World Bank, 2024
Adult literacy rate	58.9%	World Bank, 2021
Life expectancy	67.8 years	World Bank, 2024

Source: World Bank Open Data (CC BY 4.0); ILO ILOSTAT (CC BY 4.0). All figures are the most recent year available per indicator.

The 24.1% female labour force participation rate is one of the lowest in South Asia, reflecting structural and cultural constraints on women entering formal employment. This matters for AI displacement: the formal sector women who have broken into banking, telecoms, and government roles are disproportionately in the clerical occupations (8.5/10 AI exposure) that face the most near-term automation pressure. A 58.9% literacy rate (2021) also limits the population that can effectively engage with digital retraining programmes.

How WorldJobsData scores Pakistan's AI disruption risk

AI disruption timeline: Distant (12+ years). Pakistan scores 0.3/10 on risk velocity. Despite a globally connected IT and freelance sector, the overall economy is too dominated by informal and agricultural employment for large-scale formal AI displacement to happen quickly. The formal-sector cliff edge (banking, telecoms, government) is a real near-term risk for that minority of workers.
Recovery resilience: Medium (4.4/10). Pakistan has some retraining infrastructure, a growing IT skills ecosystem, and a large diaspora creating capital flows, but a 58.9% literacy rate and a 23% poverty headcount limit the proportion of workers who can effectively access digital upskilling. Recovery capacity is uneven across urban and rural populations.
Demographic factor: Entry-level job destroyer. Pakistan's population is growing fast and extremely young. Approximately 3.5 million new workers enter the labour market annually. AI adoption in formal-sector entry-level roles (data entry, back-office clerical) is reducing the traditional first step into formal employment precisely when the youth cohort is largest.

These composite scores are derived from World Bank economic indicators and WorldJobsData's AI disruption model. They are estimates, not official predictions, and are intended to provide directional context rather than precise forecasts.

Was this analysis useful?

Methodology

Employment data sourced from ILO ILOSTAT, based on the Pakistan Bureau of Statistics (PBS) Labour Force Survey 2023 (released 2024). Coverage: 77.6 million employed workers classified by ISCO-08 major occupation groups (1-digit). AI exposure scores (0-10) reflect the proportion of tasks within each occupation group susceptible to large language model capabilities including text generation, classification, data analysis and decision support. Robotics scores reflect susceptibility to physical automation. Weighted average (3.31/10) is calculated as employment-weighted mean across all occupation groups. Individual occupation scores are analytical estimates based on ILO task taxonomy research and should not be read as precise probabilities of job loss.