Top 10 AI Prompts and Use Cases and in the Government Industry in Brazil

AI is no longer a distant promise for Brazil's public sector but a national priority: the Brazilian Artificial Intelligence Plan (PBIA) commits R$23 billion through 2028 and funds shared infrastructure like a top‑tier supercomputer to power analytics for health, social benefits, urban safety and judicial case triage (Brazilian Artificial Intelligence Plan (PBIA) final version overview); at the same time, detailed legal and governance guidance - from the ANPD's data rules to the risk‑based framework in Bill No.

2,338/2023 - is mapped in the country's AI practice guide (Artificial Intelligence 2025 Brazil practice guide: trends and developments).

That policy + hardware push creates real public‑service wins (faster grant decisions, predictive health planning) but also real risks (bias, surveillance, data protection), so practical upskilling matters: a focused 15‑week AI Essentials for Work bootcamp teaches prompt writing, tool use and governance skills needed to deploy responsible AI in government (AI Essentials for Work bootcamp registration).

Program	Length	Cost (early bird)	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for the AI Essentials for Work bootcamp

Selection of the top 10 prompts and use cases followed a practical, Brazil‑centric filter: priority went to applications that map directly onto national policy and enforcement levers (the PBIA's public‑sector investments and the draft Bill No.

2,338/2023's risk‑based regime), intersect with LGPD obligations, and would plausibly require an algorithmic impact assessment or ANPD oversight in practice; sources such as White & Case AI Watch Brazil regulatory tracker show the regulatory uncertainty but also the heavy compliance stakes, including fines up to R$50,000,000, so cases with clear governance paths were favoured.

Emphasis was placed on high‑value, high‑public‑interest domains called out repeatedly in the literature - health, social benefits, public safety and procurement - where the draft law, Chambers' AI guide and practitioner notes require transparency, human oversight and pre‑market risk classification (Chambers Artificial Intelligence 2025 Brazil practice guide).

Finally, each prompt was tested for technical feasibility, bias‑mitigation needs, procurement readiness (audit and audit‑right clauses), and a measurable “so‑what?” impact on citizens - for example, favouring designs that replace brittle manual eligibility checks with documented, auditable algorithmic assessments rather than opaque scoring.

The Ministry of Health sits on one of the most concrete opportunities to turn policy into impact: Complementary Law No. 197/2022 frees roughly BRL 2.2 billion in pre‑2018 National Health Fund (FNS) balances for municipalities and states to modernize clinics, buy equipment or cover operational gaps - but a year‑end clock is ticking on those accounts, and opaque, manual applications risk leaving money unspent; smarter workflows that auto‑validate eligibility, pre‑populate the few required usage reports, and produce auditable decision trails can turn that latent cash into visible upgrades in months rather than years.

Brazil's track record of large, mission‑oriented funding - illustrated by the Ministry's Grand Challenges Brazil partnership (PPP$ 48.61M across 75 studies) - shows public grants can seed scaled innovations when disbursement is predictable and transparent.

New regulatory reforms for research (Law 14.874/24) add faster clinical trial timelines that complement grant agility, so combining clearer rules with programmatic automation and human oversight can both protect LGPD compliance and accelerate population‑level wins.

For implementation playbooks and the underlying grant details, see the National Health Fund guidance and the Grand Challenges Brazil funding analysis.

Item	Key figure
FNS pre‑2018 balances available	BRL 2.2 billion
Grand Challenges Brazil (2011–2020)	PPP$ 48.61 million - 75 studies
Clinical Research Law 14.874/24 regulatory timeline	90 business days for trial application response

“Brazil is one of the largest markets for the pharmaceutical industry. This gives us a very broad potential basis for drug development, but it's not happening.”

São Paulo can amplify resident-facing services by scaling citizen service virtual assistants into a true “city brain”: chat and WhatsApp bots that handle routine queries and grievances, triage requests to the right department, and feed real‑time signals into an integrated municipal command centre so problems get fixed before residents even have to report them - an idea echoed by cities joining Bloomberg Philanthropies' City Data Alliance, which now supports São Paulo's push to use data, analytics and AI to modernize resident services (Bloomberg Philanthropies City Data Alliance); success depends on building the right platform, privacy protections and interoperability standards described in Deloitte's vision of “city operations through AI” and on practical delivery patterns such as WhatsApp grievance bots and citizen web portals (City Operations Through AI, AI-powered citizen service delivery).

When paired with procurement safeguards and workforce reskilling, these assistants can reduce wait times, cut administrative overhead, and make everyday interactions - permits, complaints, benefit queries - feel faster, friendlier and more accountable to the people of São Paulo.

“What we have been working on is the transformation of data into relevant information for strategic decisions that we can make. This will improve immensely the governance and the efficiency of the city and ultimately the transparency of the decisions made by politicians or by public authorities.”

Ministério da Cidadania faces a clear opportunity - and a cautionary tale - when adopting AI for grant, benefits and welfare eligibility scoring: models can cut queues and prioritize scarce resources, but Brazil's experience with INSS automation shows speed without safeguards can harm vulnerable people (one widely reported case saw a retirement claim rejected in six minutes), producing hundreds of thousands of automatic rejections and sharper inequality; audit‑backed research recommends transparency, human review and co‑design to keep services fair (Audit analysis of INSS automation and impact on benefit rejections in Brazil).

Aligning any scoring system with LGPD and the ANPD's guidance matters too: the ANPD's Preliminary Study on Generative AI frames how necessity, anonymisation and transparency must shape public‑sector ML pipelines to avoid unlawful scraping, hidden training data and unexamined inference risks (ANPD preliminary study on generative AI and data protection in Brazil).

Finally, Brazil's emerging AI law (Bill 2,338/2023) and its emphasis on algorithmic impact assessments, contestability and public databases underline practical steps: publish risk assessments, keep human‑in‑the‑loop appeal routes, and design eligibility scores so they reduce - not reinforce - social exclusion (Technical analysis of Brazil Bill PL 2338/2023 AI legislation and algorithmic impact assessments).

Item	Figure
Automated decisions (2022)	1,325,387
Automatic rejections (2022)	>800,000
Share of automatic decisions (Dec 2022)	45%

“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.”

State health secretariats can turn messy, regionalized hospital data into life‑saving action by using AI to forecast demand and drive dynamic resource allocation: simulations of hospital, ICU and ventilator need show stark regional disparities - Brazil averaged 2.3 beds per 1,000 people (≈12.2 beds per 10,000) but some micro‑regions are “hospital deserts,” private beds are absent in dozens of areas, and mean distance to an ICU inside the same macro‑region is 98 km (in Amazonas the average jumps to 615 km), so smart forecasting isn't academic, it's the difference between a timely transfer and a fatal delay.

Machine‑learning models calibrated to regional infection trajectories can pre‑position field hospitals, reassign staff, and trigger public‑private bed sharing before local capacity collapses; for example, simulations show that at a 1% infection rate over one month many micro‑regions exhaust general‑bed capacity and ICU and ventilator shortages become nearly universal in pessimistic scenarios.

Practical playbooks - dynamic bed regulation, real‑time CNES integration and scenario‑based alerts - follow directly from this evidence base (see the CNES/Cad. Saúde Pública analysis of simulated demand and the broader literature on critical‑care shortages in Brazil).

Item	Figure
Mean hospital beds per 10,000	12.2
Adult ICU beds (2019)	34,464 (48% available to SUS)
Ventilators (2019)	57,303 (72% public)
Mean distance to ICU (same macro‑region)	98 km (Amazonas mean 615 km)
Macro‑regions exceeding ICU capacity (1% in 1 month)	≈100% (pessimistic scenario)
Macro‑regions exhausting ventilators (pessimistic)	97%

São Paulo's Smart Sampa has turned the city's sidewalks into a live, city‑scale experiment in public‑safety AI: with roughly 25,000 cameras feeding alerts that officials say helped locate missing people and flag fugitives, the program's early results are dramatic - and divisive.

Proponents point to faster arrests and a so‑called “prisonometer” outside the command centre that makes outcomes visible, but critics warn that mass biometric scanning - operating with 30‑day image retention and heavy private‑sector ties - creates the very privacy and bias risks that Brazilian cases and scholarship have repeatedly flagged; the policy challenge is to capture the public‑safety gains without normalizing opaque, racially skewed surveillance.

Practical next steps include tighter transparency (where, when and why cameras match), auditable match thresholds, human‑in‑the‑loop checks and enforceable procurement safeguards so vendors can't hide critical details behind trade secrets - lessons underscored in regional analyses and regulatory briefings such as Chatham House's review of facial‑recognition rollouts and detailed reporting on Smart Sampa in El País (Chatham House: regulating facial recognition in Latin America, El País: Smart Sampa coverage).

Item	Figure
Approx. cameras	~25,000
Fugitives arrested (reported)	1,044
Images retention	30 days
Operational cost	≈ BRL 10 million/month

“people have already learned to live with video surveillance… ‘Smile, you are being protected'”

Brazil's audit bench and local procurement teams can leap from paperwork to proactive oversight by pairing tried procurement law rules with AI‑driven contract systems: the Tribunal de Contas da União (TCU) has promoted standardized, open‑data indicators that flag risks like single bids or discretionary procedures (TCU public procurement good-practices brief), while modern contract platforms show how automation - dynamic dashboards, templates, concurrent reviews and e‑signatures - turns slow, opaque approval chains into auditable workflows (see a practical contracts case study for similar institutions: JAGGAER Contracts success story (Texas Christian University)).

In Brazil this matters not just for efficiency but for legality: procurement law sets clear thresholds and publication, impartiality and transparency principles that must drive any automation design (Summary of Brazilian public procurement rules and thresholds).

The “so what” is visceral - a procurement dashboard that lights up the instant a lone bidder appears can shrink a months‑long audit hunt into a 60‑second investigation - yet safeguards (audit rights, human‑in‑the‑loop reviews, contestability clauses) must be baked into contracts so automation strengthens accountability instead of hiding it.

Item	Figure / Example
Tender waiver threshold (engineering)	R$15,000
Tender waiver threshold (other services/purchases)	R$8,000
Automation benefits (example)	dynamic dashboards, templates, concurrent reviews, e‑signatures, error reduction

“To me, the biggest advantage of JAGGAER is effective use of resources and time across the university, allowing people to spend less time and energy but get a better result.”

Courts and prosecutors are already piloting legal‑tech that can shave months off case backlogs - but Brazil's playbook stresses guardrails over glamour: the National Council of Justice (CNJ) framed governance in Resolution No.

332/2020 and, by 2023, 62 courts had adopted or were testing AI projects (a 17% rise year‑on‑year) to automate document classification, case indexing and process monitoring; flagship tools like the STF's “Victor” system now classify extraordinary appeals and help prioritize dockets, turning paper mountains into searchable signals while exposing hard questions about transparency, bias and privacy under the LGPD. Recent rulemaking reinforces human oversight - new CNJ regulations explicitly require human supervision of judicial AI - and legal teams and procurement officers must bake in contestability, audit rights and explainability when contracting systems.

Practical guidance on these procurement and governance patterns can be found in the CNJ coverage and follow‑ups on regulatory approvals (Artificial Intelligence and the Brazilian Judiciary: Challenges and Opportunities) and in reporting on the CNJ's 2025 regulation (CNJ regulation for AI in the Brazilian Judiciary (March 2025)), while procurement playbooks help teams convert oversight into enforceable contract terms (AI procurement best practices for government contracting).

Metric	Value
Courts with AI projects (2023)	62
Year‑over‑year increase	17%
Key governance instrument	CNJ Resolution No. 332/2020

Transport regulators - DENATRAN and ANTT - are the natural linchpins for turning pilots into safe, nationwide deployments of automated driving systems (ADS): regulators already flagged AV rulemaking on DENATRAN's 2021–2022 agenda and São Paulo's Level‑3 taxi trials show local innovation racing ahead of national law, so harmonized rules must bridge testing, road‑rule updates and infrastructure adaptation (Autonomous Vehicles: Current and Future Regulatory Challenges, Global AV policy snapshot noting São Paulo Level‑3 trials).

Recent parliamentary movement adds muscle to that agenda: a unified bill approved in the Chamber's Road & Transportation Committee sets strict gates for circulation - prior authorization, supervised test routes, continuous fault‑monitoring and mandatory full insurance - and tasks public bodies with adapting signage, traffic control and concession contracts so ADS can operate safely on Brazilian roads (Brazil approved autonomous vehicle bill summary).

Practical harmonization will also lean on international homologation practice and technical standards (CSMS, SUMS, ALKS and ISO safety/cyber norms) to specify vehicle approvals, software update regimes and “black‑box” diagnostics - picture a fleet where a single fault‑alert can trigger a safe‑mode handover and a real‑time road‑authority alert, preventing the next headline‑making crash (Homologation and autonomous driving safety standards guidance).

Item	Key point
DENATRAN agenda	AV regulation included (2021–2022)
São Paulo trials	Level‑3 AV taxis testing (2024)
New bill (Aug 2025 committee approval)	Authorization, supervised tests, continuous monitoring, mandatory insurance, infrastructure adaptation
Homologation standards	CSMS, SUMS, ALKS; ISO 26262 / SOTIF / ISO/SAE 21434

INPI and municipal archives can unlock huge public value by treating documents as structured, searchable knowledge rather than locked PDFs: academic work such as the RegBR project shows how a national framework to centralize, classify and analyze regulations enables fast, semantically rich queries across legal texts (RegBR framework to classify Brazilian regulations (PLOS ONE)), while community efforts that model roughly 28,000 laws in Wikidata prove the practical payoff of open, linked legislation for transparency and reuse (Modeling Brazilian legislation in Wikidata (~28,000 laws)).

Converging these patterns into INPI and city archives - automatic metadata extraction, entity linking (companies, statutes, patent families), and semantic search - creates an auditable, interoperable corpus that speeds patent examination, eases FOIA requests and powers civic apps; it also lays the groundwork for legally compliant AI oversight, since Brazil's draft AI law already contemplates public registries and impact assessments for high‑risk systems (Analysis of Brazil's proposed AI law: public registries and governance), turning a scattered archive into a queryable national memory where a single search can replace hours of manual hunting.

For the Ministry of Defence and the EBIA, layered AI‑driven anomaly detection turns routine network and sensor noise into actionable national‑security signals: adaptive baselining and behavior models spot the quiet border camera or IoT sensor that suddenly begins sending large files to unknown hosts - an early red flag that signature tools would miss - while flow‑level monitoring and selective deep‑packet inspection let analysts separate false positives from real intrusions without choking operational bandwidth.

These systems pair real‑time alerts and automated playbooks to blunt DDoS and APTs, feed consolidated evidence into SIEM/SOAR workflows for faster incident handling, and preserve full‑fidelity flow records for post‑event forensics, so a single anomalous overnight spike becomes an auditable trail instead of a mystery.

Critical design choices for Brazilian defence deployments include adaptive baselining that learns seasonal and mission rhythms, vendor‑neutral integration with existing command‑and‑control, and clear escalation rules so human analysts retain final authority; practical best practices and technical patterns are summarized in network anomaly detection guides from Kentik network anomaly detection guide and Meter network anomaly detection guide.

Capability	Role for Defence
Adaptive baselining	Reduces false positives by learning normal mission and regional traffic
Flow monitoring + DPI	Fast, wide coverage with targeted deep inspection for high‑risk links
Real‑time SIEM/SOAR integration	Automated mitigations and analyst workflows to shorten MTTD/MTTR
Forensic retention	Auditable trail for investigations and legal/regulatory review

Practical next steps for Brazil's public sector are straightforward: treat governance as part of delivery, not an afterthought - mandate preliminary risk classification and public algorithmic impact assessments for high‑risk systems, lock human‑in‑the‑loop review into every automated decision that affects benefits or liberty, and build procurement contracts with explicit audit rights and data‑provenance warranties so vendors can't hide training data behind trade secrets; these measures map directly onto the risk‑based approach in the proposed AI law and related guidance (Chambers AI 2025 Brazil trends and developments report) and respond to enforcement incentives (penalties of up to R$50 million in recent analyses) that make compliance material (Overview of Brazil's AI Act and enforcement penalties).

Parallel investments in capacity - sandboxed pilots, continuous auditing and workforce reskilling - turn obligations into capabilities; for teams starting today, a focused, practical course like the AI Essentials for Work bootcamp helps staff learn promptcraft, impact assessment basics and governance patterns needed to operationalize these guardrails (Nucamp AI Essentials for Work bootcamp registration), so Brazil can capture PBIA‑funded AI gains while keeping rights, transparency and accountability front and centre.

Program	Length	Cost (early bird)	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for the Nucamp AI Essentials for Work bootcamp