Brazil AI Job Risk 2026: Which of 102 Million Workers Are Most Exposed?

102 million workers, 10 major occupation groups

ILO ILOSTAT - Brazil's international workforce data source, published under a Creative Commons CC BY 4.0 licence - tracks Brazilian employment using the ISCO-08 international occupation classification. Unlike Eurostat's detailed sub-group data for European countries, Brazil's ILO data covers 10 major occupation groups (ISCO 1-digit level). This gives us a clear picture of broad trends across the economy, from managers and professionals through to elementary workers and agricultural labourers.

Brazil's workforce has grown significantly in the past decade. In 2012, total employment was approximately 87.5 million. By 2025 it had reached 102.1 million - a net increase of 14.6 million workers. The growth has not been uniform. Professionals and service workers have expanded substantially, while craft and trades workers have grown more slowly and agricultural workers have slightly declined. This structural shift toward white-collar and service roles is exactly the shift that increases a country's aggregate AI exposure over time.

The most AI-exposed jobs in Brazil

Brazil's highest AI exposure group is clerical support workers at 8.5/10 - matching France's score for the same broad occupation group, and close to Germany's 9.0/10 for general clerks specifically. This covers 8.6 million Brazilian workers: data entry operators, administrative assistants, accounting clerks, customer service representatives, and the broad range of office workers who process, classify, and manage structured information.

These roles share a common characteristic: the core daily tasks are highly structured, repetitive, and information-based. Entering purchase orders into an ERP system, responding to standard customer queries, processing invoices, and filing documents are tasks that current AI tools perform reliably. Across Brazil's formal economy - in banks, retail chains, public agencies, telecommunications companies, and large manufacturers - AI tools that automate clerical work are already being deployed.

Professionals score 6.5/10 on AI exposure, covering 13.7 million workers. Brazil's professional class has grown faster than almost any other group over the past decade, driven by a large cohort of software developers, financial analysts, accountants, engineers, and healthcare workers. The AI exposure here is concentrated in the knowledge-work elements: code generation, financial modelling, report drafting, and data analysis. Physical and relational elements of professional work - the hospital consultation, the engineering site visit, the client relationship - are much less exposed.

Brazil's service economy and the AI question

Service and sales workers are Brazil's largest occupation group at 22.7 million - nearly a quarter of the entire workforce. They score 3.5/10 on AI exposure, which sounds reassuring. But the picture is more complex than the score suggests. Within this broad group are retail cashiers facing self-checkout, food service workers facing kitchen automation, call centre agents facing AI chatbots, and hotel staff facing AI-assisted booking systems.

The 3.5/10 score reflects the fact that many service and sales tasks require physical presence, direct human interaction, and real-time adaptation to unpredictable situations - all things that current AI handles poorly. A street market vendor in Sao Paulo or a doorstep salesperson in Bahia is not threatened by current AI in any meaningful way. But a call centre agent handling structured queries in Sao Paulo's business district faces a much more immediate risk from AI automation tools that are already deployed by Brazilian telecoms and banks.

Brazil has invested heavily in contact centre infrastructure over the past two decades, with millions of workers employed in formal call centre roles at companies like Atento, Concentrix, and Teleperformance. These roles score significantly higher on AI exposure than the service group average. The major telecoms - Claro, TIM, Vivo - and large banks including Itau, Bradesco, and Nubank are actively deploying AI-driven customer service tools. Displacement in this sub-sector has already begun.

Manufacturing and the robotics risk in Brazil's industrial heartland

Brazil's industrial sector - concentrated in Sao Paulo state, the greater ABC manufacturing region, and the south of the country - employs around 9.7 million plant and machine operators. This group scores just 3.0/10 on AI exposure but 7.5/10 on robotics risk - the highest in Brazil. The threat here is not software. It is physical automation: industrial robots, CNC machines, automated assembly lines, and increasingly autonomous material handling systems.

Brazil's automotive industry is the world's eighth largest by production volume. Volkswagen, General Motors, Toyota, and Stellantis all operate major Brazilian manufacturing facilities. These plants have been automating for decades, and the cost trajectory of industrial robots continues to decline. The International Federation of Robotics (IFR) tracks Brazil as one of the fastest-growing markets for industrial robot installations in Latin America. This is not a future risk - it is an ongoing structural shift that has been reshaping Brazil's industrial employment for fifteen years.

Craft and trades workers score 2.5/10 on AI and 4.5/10 on robotics, covering 13.4 million people. Brazil's construction sector is the largest employer within this group - and Brazilian construction remains largely manual, operating at a labour cost structure where automation has historically not been economically competitive. This is changing slowly, but the pace is much slower than in Europe or the US. For now, skilled trades workers in Brazil face a moderate medium-term risk rather than an immediate one.

Brazil's growing professional class: augmentation before replacement

No group illustrates Brazil's economic transformation more clearly than professionals - the 13.7 million workers who in 2025 form Brazil's largest growing occupation segment. In 2012, this group numbered 8.4 million. By 2025 it had grown to 13.7 million - adding 5.3 million new professional workers in thirteen years. This expansion reflects the growth of Brazil's technology sector, expansion of financial services, increased demand for healthcare workers, and the rapid scaling of education.

Brazil's tech sector has grown dramatically. Companies like Nubank (digital banking), iFood (food delivery), TOTVS (enterprise software), and a rapidly growing startup ecosystem centred on Sao Paulo have created hundreds of thousands of software engineering, product management, and data science roles that did not exist a decade ago. Brazil now produces more software developers than any other country in Latin America.

These workers score 6.5/10 on AI exposure - high enough to be a genuine concern, but important to contextualise. The AI exposure of a software developer is not the same as the AI exposure of a data entry clerk. The developer's AI tools (coding assistants, automated testing, documentation generators) augment rather than replace - at least for now. Senior engineers who understand the systems, define the architecture, and catch the AI's mistakes are more valuable in an AI-augmented environment, not less. The pressure is on junior and entry-level roles, where AI can already replicate much of the output.

Agricultural workers: low AI, high robotics, and genuine complexity

Brazil is the world's largest producer of coffee, sugarcane, soybeans, and orange juice. Its agricultural sector employs 4.6 million skilled agricultural workers, who score 3.0/10 on AI exposure but 6.5/10 on robotics risk. The gap between these two scores captures something important about Brazilian agriculture: it is not AI that threatens it, but precision agriculture machinery and harvesting automation.

Brazilian agribusiness - the large-scale commercial farming operations in Mato Grosso, Parana, and Sao Paulo state - is already highly mechanised. Soybean and sugarcane harvesting are largely automated through GPS-guided harvesting equipment. The workers who have already been displaced by this automation over the past twenty years are not counted in the current workforce figures - they moved to cities or into informal work years ago. The current agricultural workforce represents those in roles that machinery has not yet reached: smaller-scale farms, horticultural work, coffee picking on steep terrain, and livestock management.

The IBGE (Brazilian Institute of Geography and Statistics), Brazil's national statistics agency, tracks this sector closely. Their data shows that while large-scale commercial agriculture has steadily shed workers through mechanisation, small-scale farming remains labour-intensive and is likely to remain so for the foreseeable future.

What the 35.6% informal rate means for AI disruption

Just over a third of Brazilian workers operate outside the formal employment system - without a signed employment contract, labour protections, or access to public retraining programmes. This shapes how AI disruption arrives in Brazil in two important ways.

First, informal workers in high-exposure roles face displacement without safety nets. A formal bank clerk whose role is automated has access to unemployment insurance (seguro-desemprego) and potentially SENAI or SENAC retraining programmes. An informal bookkeeper for a small family business who is replaced by accounting software has no equivalent support. The ILO estimates Brazil's informal economy at 35.6% of the workforce in 2025 - a slight decline from earlier years, but still affecting approximately 36 million workers.

Second, some informal roles are paradoxically more resilient to AI displacement. A street food vendor, a domestic worker, a building maintenance worker operating informally - these roles require physical presence, local relationship knowledge, and real-time adaptation that AI cannot replicate. The informality itself, which makes these workers economically vulnerable in many ways, also means they are often doing the kinds of tasks that are hardest to automate.