Top 5 Jobs in Government That Are Most at Risk from AI in Brazil - And How to Adapt

AI is reshaping how Brazil's public sector works: the Plano Brasileiro de Inteligência Artificial (PBIA) promises roughly R$23 billion through 2028 to scale AI across health, administration and public services, putting routine roles - from call‑center agents and registry clerks to document reviewers - squarely in the automation spotlight (see PBIA coverage for health and SUS use cases).

As legal and compliance frameworks evolve (Bill No 2,338/2023 and LGPD guidance), public servants must balance efficiency gains with transparency and rights protections - details covered in the broader legal and regulatory landscape.

Meanwhile, reliance on global cloud providers and the rapid build‑out of data centres add sovereignty and environmental questions (one proposed site's power draw was compared to millions of residents).

Upskilling is a practical defense: the AI Essentials for Work bootcamp teaches promptcraft and workplace AI skills to help civil servants pivot from repetitive tasks to higher‑value oversight and human-centered services.

“In the digital age, underdevelopment is measured by the number of gigabytes stored and processed in the clouds of a handful of companies.”

Selection of the top five at‑risk government roles used a practical, Brazil‑focused methodology: start with the draft Brazilian AI Act's risk‑based framework - whose high‑risk bucket (from recruitment and service eligibility to migration and biometric systems) flags public‑sector tools requiring algorithmic impact assessments - and layer on the ANPD's six‑step weighting for high‑risk data processing to measure rights‑impact and likelihood of automation (Brazil's AI Act: a risk‑based approach; ANPD six‑step methodology).

Roles were prioritised where rule‑driven tasks meet high‑volume data handling or decisions that can cause tangible harm - exactly the scenarios the law targets - while reskilling paths were chosen to move workers toward oversight: how to run AI inventories, do bias testing, document algorithmic impact assessments, and keep human‑in‑the‑loop workflows (start with practical controls like inventories and monitoring to build a defensible foundation).

so what?

The so what? is stark: agencies that fail to adapt face not only service breakdowns but regulatory exposure (including steep fines), so retraining for oversight and auditability is a practical, protective investment.

Public‑service customer‑service agents - those answering phones, staffing service desks and handling routine in‑person requests - face one of the clearest automation pressures because much of their work is rule‑driven and high‑volume: AI chatbots can answer FAQs, guide users through forms, schedule appointments and provide 24/7 multilingual help, cutting wait times and repetitive load so humans can focus on complex exceptions and sensitive cases; studies cited by Infobip even point to massive productivity gains if routine tasks are automated (large savings in hours and costs).

Brazil already has practical examples: the TSE's WhatsApp virtual assistant (built with Infobip Answers) handles 16 election queries and helps clarify fake news, showing how bots can scale basic civic information.

But deployment carries risks - data collection, bias, privacy and the need for human‑in‑the‑loop oversight - so start with defensible controls: build AI inventories, monitoring and bias testing and follow ethical guidance like the TCU recommendations to keep savings from turning into legal or trust liabilities.

For a practical how‑to, see Infobip review of government chatbots and the Nucamp AI Essentials for Work syllabus - operational AI controls guide.

Gartner: - 60% of government organizations will prioritize business process automation by 2026

Administrative and registry clerks - who spend hours copying data between legacy systems, filling forms and scheduling appointments - are prime candidates for quick automation wins through Robotic Process Automation: RPA bots can mimic repetitive UI actions to move records, generate reports and scale during demand spikes with low setup costs and a gentler learning curve (Robotic Process Automation benefits for government services).

Brazil's experience shows the trade-offs: the INSS sped up some decisions (even granting a death pension in 12 hours) but also saw dramatic increases in automatic rejections and a famous case where a retirement claim was denied in six minutes, highlighting how brittle rule‑based automation can be when databases are incomplete (Analysis of INSS automation and public interest trade-offs).

For clerks, the practical pivot is clear: learn to design and monitor RPA workflows, run AI inventories and bias checks, and own exception triage so automation handles the repeatable work while humans defend rights and fix messy records - start with operational controls and inventories to turn disruption into a shield for citizens and jobs (Operational AI controls and inventories for government agencies).

"The automated analysis of benefit requests is one of the actions that Social Security has adopted to reduce the response time for citizens requesting a service or benefit."

Data‑processing clerks, statistical assistants and routine analysts in Brazil are squarely in AI's path because so much of their work - cleaning records, running batch analyses and flagging exceptions - is rule‑driven and easy to scale with models and automation now being rolled into tax, health and social‑welfare systems; public authorities already deploy AI for analytics and process automation, so the same tools that speed reporting can also embed biased patterns unless agencies build safeguards (see Brazil's evolving legal framework and bias risks at Artificial Intelligence 2025 - Brazil: Trends and Developments on AI and Legal Risks).

The policy answer is practical: start procurement and projects with solid data governance - AI inventories, DPIAs/algorithmic impact assessments, bias testing, contractual assurances on data provenance - and train clerks to own exception triage and monitoring rather than purely repetitive inputs; the World Economic Forum's “AI Procurement in a Box” lessons from São Paulo and Hospital das Clínicas show how procurement plus data‑maturity steps (data lakes, staff training) turn automation from a job threat into an efficiency tool that still protects rights (World Economic Forum - AI procurement in the public sector: Lessons from Brazil).

Remember: under the LGPD people can request correction of inaccurate outputs, yet models also embed training data into their parameters - so preserving the right to fix errors must be part of any clerk's new toolkit.

“Why can't a country with 200 million people, a nation 524 years old with a globally respected intellectual foundation, create its own mechanisms instead of relying on AI from China, the United States, South Korea, or Japan? Why can't we have our own?”

Judicial and paralegal assistants and court document reviewers are already feeling the pressure as Brazil's highest courts deploy tools like MARIA to draft sentencing minutes, summarize magistrates' opinions and even generate multimedia content inside STF‑Digital - work that used to be labor‑intensive is now routinely surfaced as machine‑drafts that must be checked, contextualized and auditable (see the detailed MarIA overview on SSRN and press coverage of the STF rollout).

This shift can speed case flow and shrink backlogs, but it also raises familiar governance hazards: algorithmic opacity, accountability gaps, biased precedents and the risk that non‑institutional tools leak into casework without trace.

The practical pivot for assistants is concrete and immediate - become the human in the loop: verify sources, annotate and correct AI drafts, insist on recorded provenance and keep algorithmic impact checks in the workflow - steps that protect litigants' rights while preserving jobs that add legal judgement rather than just typing.

For background on systemic risks and the CNJ transparency expectations, see the judiciary analysis that outlines how rapid automation can cascade through millions of cases if left unchecked.

“MARIA's objective is not to replace people, but to assist the work of the STF.”

Border‑control officers, identity‑verification clerks and surveillance analysts are on the front line of Brazil's biometric rush: the ANPD opened a public consultation to clarify how sensitive biometric data (fingerprints, faces, voice) should be processed under the LGPD - formally launched on June 2, 2025 with a tight participation window - and the legal pressure to get governance right is real (biometric templates are treated as sensitive data and demand higher safeguards).

At the same time policymakers are rolling out large‑scale deployments: a June 14, 2025 sports‑law mandate now requires face biometrics at stadium turnstiles for venues over 20,000, systems that vendors say can validate entries in roughly two seconds and push some gates to 25 accesses per minute, while police integrations have already enabled arrests.

Those speed and security gains come with hard tradeoffs - irreversible leaks of face data, biased matches and opaque vendor pipelines - so practical reskilling is urgent: learn to run DPIAs, keep auditable provenance logs, demand contractual data‑sovereignty clauses, and own exception reviews so human judgment stays central when systems decide who crosses a border or enters a stadium.

For details see the ANPD public consultation and reporting on Brazil's stadium biometric mandate.

Policy / Deployment - Date - Key points / source

ANPD public consultation on biometric data protection rules - Opened: June 2, 2025 (consultation detailed) - Clarifies processing of fingerprints, facial recognition, voice under LGPD; input sought on security, limits, rights (ANPD public consultation on biometric data protection rules (ID TechWire))

Biometric ticketing mandate for stadiums - Effective: June 14, 2025 - Stadiums >20,000 must use face biometrics; vendors report ~25 accesses/min and ~2s per person; ANPD requested DPIAs from clubs (Brazil biometric ticketing mandate for stadiums (BiometricUpdate))

ANPD report on biometrics & LGPD - June 24, 2024 (report) - Biometric data = sensitive; highlights bias, security risks and public‑space controversies (ANPD biometrics and LGPD report (Mayer Brown))

Final steps for public servants in Brazil should be practical, prioritized and tied to the fast‑moving regulatory landscape: begin by mapping every deployed or planned AI (an AI inventory) and run algorithmic impact assessments/DPIAs on anything the draft AI Act would flag as high‑risk, keep automatic log registers and provenance records so decisions leave auditable “digital breadcrumbs,” and lock in human‑in‑the‑loop checkpoints for contestability and corrections - the very measures the proposed AI Act, ANPD guidance and EBIA principles emphasize (see the AI Act's risk‑based rules and ANPD oversight in Brazil's AI Act analysis and national strategy goals summarized by the OECD overview of EBIA).

Treat compliance as workforce development: train teams on inventories, bias testing and monitoring so clerks, analysts and officers can run audits and manage exceptions rather than being replaced; a short, job‑focused course like Nucamp's AI Essentials for Work teaches promptcraft, operational AI controls and workplace use cases in 15 weeks and is a concrete next step for public servants seeking to pivot into oversight roles (Nucamp AI Essentials for Work registration).

Act quickly - noncompliance can carry steep administrative penalties (up to R$50 million in the current drafts) - so build the basics now and scale governance as laws and standards mature.

“The legislation remains protective of fundamental rights.”