Key findings
- Clerical support workers score 8.5/10 on AI exposure - Bolivia's highest-risk group. Around 221,000 workers in data entry, administrative coordination, and office roles across La Paz, Cochabamba, and Santa Cruz face growing pressure from administrative AI tools.
- Professionals score 6.5/10, covering 484,000 Bolivian doctors, lawyers, engineers, and accountants - 7.1% of the workforce. Bolivia's expanding professional class, driven by increased tertiary education, faces AI augmentation in legal, financial, and diagnostic work.
- Skilled agricultural workers score just 3.0/10 on AI exposure, covering 1.53 million workers - 22.5% of Bolivian employment. Bolivia's diverse agro-ecological zones (altiplano, valleys, and lowlands) support highly varied agricultural labour that AI cannot readily automate.
- Craft and related trades workers score 2.5/10, covering 1.3 million workers - 19% of employment. Bolivia's large informal artisanal sector, including textiles, food processing, and construction trades, remains substantially AI-resilient.
- Bolivia's weighted average AI exposure of 3.57/10 reflects the dominance of informal and agricultural labour that keeps the formal AI-exposed sector proportionally small.
6.82 million workers, ILO ILOSTAT data
Employment data comes from ILO ILOSTAT (CC BY 4.0), based on INE Bolivia's Encuesta de Hogares (Household Survey). Classification follows ISCO-08 major group structure across Bolivia's 6.82 million employed workers. Bolivia does not have detailed wage data in the ILO dataset for most occupation groups, reflecting the large informal economy where wages are not systematically tracked. The formal sector - where wage data exists and AI adoption is most relevant - represents a minority of total Bolivian employment, concentrated in La Paz (government and services), Santa Cruz (agribusiness and hydrocarbons), and Cochabamba (manufacturing and trade).
Bolivia's informal economy is estimated to account for approximately 60-70% of employment by some measures, meaning a large share of the 6.82 million workers operate outside formal employment relationships. This has two important implications for AI risk: first, AI adoption is primarily a formal-sector phenomenon in the near term, so the effective AI-exposed workforce is smaller than the raw occupation data suggests. Second, workers in the informal economy have fewer institutional protections if AI displacement does occur in sectors where they work informally alongside formal workers.
The most AI-exposed jobs in Bolivia
Clerical support workers score 8.5/10 on AI exposure - the same peak score observed across all economies we analyse. Around 221,000 Bolivian workers perform data entry, document processing, scheduling, and administrative coordination in formal-sector organisations. These roles are concentrated in La Paz's government ministries and financial institutions (Banco de Bolivia, BancoSol, BISA), in Cochabamba's commercial sector, and in Santa Cruz's agribusiness and energy companies.
The pace of AI adoption in Bolivian businesses is slower than in Chile, Colombia, or Peru due to lower digital infrastructure penetration and cost constraints. However, the adoption gap is closing: Bolivia's digital economy strategy and expanding 4G coverage (now reaching over 70% of the population) are enabling cloud-based administrative software to reach smaller Bolivian businesses. When adoption accelerates, the 221,000 clerical workers face the same task displacement pressure as their counterparts in more developed Latin American economies.
Professionals at 6.5/10 cover 484,000 workers - the largest high-exposure group in Bolivia by headcount. Bolivia's professional class includes a growing number of lawyers, accountants, civil engineers, and doctors who use digital tools in their work. AI applications in legal document review, financial analysis, structural engineering calculations, and medical diagnostics are reaching Bolivian professionals through international software platforms. The effect is primarily augmentation now - AI tools making professionals more productive - but the longer-run competitive pressure on entry-level professional work is real.
| Occupation Group (ISCO-08) | AI Score | Robotics Risk | Workers | % of Total |
|---|---|---|---|---|
| Clerical support workers (4) | 8.5/10 | 2.5/10 | 221k | 3.2% |
| Professionals (2) | 6.5/10 | 1.5/10 | 484k | 7.1% |
| Managers (1) | 5.5/10 | 1.5/10 | 114k | 1.7% |
| Technicians and assoc. professionals (3) | 5.5/10 | 3.5/10 | 418k | 6.1% |
| Service and sales workers (5) | 3.5/10 | 4.5/10 | 1,606k | 23.5% |
| Skilled agricultural workers (6) | 3.0/10 | 6.5/10 | 1,534k | 22.5% |
| Plant and machine operators (8) | 3.0/10 | 7.5/10 | 604k | 8.9% |
| Craft and related trades workers (7) | 2.5/10 | 4.5/10 | 1,299k | 19.0% |
| Elementary occupations (9) | 2.0/10 | 5.5/10 | 537k | 7.9% |
Bolivia's informal economy - estimated at 60-70% of employment - is a structural AI buffer. AI adoption happens primarily in formal organisations. The informal majority operates largely outside the near-term displacement zone.
Service workers and the informal economy
Service and sales workers score 3.5/10 on AI exposure and cover 1.6 million workers - 23.5% of Bolivian employment, the single largest occupation group. This group includes street vendors, market traders, restaurant workers, domestic workers, and retail staff. In Bolivia, a substantial portion of this group operates informally - family-run stalls, informal domestic employment, and street commerce that has no formal employment relationship and where AI productivity tools are not relevant in the near term.
The Bolivian government has maintained social policies including minimum wage increases and the Renta Dignidad pension scheme that provide some income floor for low-income workers. These policies do not directly address AI displacement, but they do mean that formal-sector AI displacement would push workers toward an informal sector that remains large and absorptive, rather than creating the kind of unemployment spike seen in economies with smaller informal buffers.
What this means for Bolivian workers
Bolivia's AI exposure profile is broadly favourable in aggregate: a 3.57/10 weighted average is well below global averages, driven by the large agricultural and informal sectors. The risk is real but concentrated. For the 221,000 formal-sector clerical workers and 484,000 professionals in La Paz, Cochabamba, and Santa Cruz, AI augmentation and eventual displacement risk is as real as anywhere in Latin America - just applied to a smaller share of the total workforce than in Chile, Colombia, or Peru.
The structural risk for Bolivia is longer-run: as formalisation increases, as digital infrastructure expands, and as the professional and service sectors grow as a share of employment, Bolivia's AI exposure profile will shift upward. The demographic bulge of young Bolivians entering the labour market over the next decade will enter a workforce where AI adoption in formal employment is accelerating. Career planning for this cohort should account for AI augmentation in professional, clerical, and technician roles as a given rather than a contingency.
See Bolivia's full occupation breakdown
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Methodology
Employment figures are from ILO ILOSTAT (CC BY 4.0), based on INE Bolivia's Encuesta de Hogares, using ISCO-08 major group classifications. Data covers approximately 6.82 million Bolivian workers. AI exposure scores are research-based estimates per ISCO-08 group, informed by Frey-Osborne (Oxford), OECD, and IMF studies on task-level automation. They reflect the proportion of an occupation's core tasks that current AI can perform or significantly augment - not predictions of job loss rates.
Frequently asked questions
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Related analyses
Data sources
- ILO ILOSTAT - Employment by sex, occupation (ISCO-08), Bolivia (CC BY 4.0)
- INE Bolivia - Instituto Nacional de Estadistica, Encuesta de Hogares
- Frey, C.B. and Osborne, M.A. (2017). The future of employment. Technological Forecasting and Social Change.
- OECD - The Future of Work and Skills
- IMF - Gen-AI: Artificial Intelligence and the Future of Work (2024)