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

Peru's July 2023 Law 31814 put AI squarely on the public‑sector agenda, promoting AI

for the economic and social development of the country

while mandating a risk‑based approach, human oversight, transparency and centralized governance under the digital transformation system - steps that aim to make Peru a regional leader in responsible AI adoption.

That balance - boosting innovation in citizen services, fraud detection and rural health while restricting unacceptable risks like certain biometric and social‑scoring uses - creates both opportunity and work: public agencies must budget for audits, documentation and staff training to meet compliance requirements.

For teams looking to apply AI practically and safely, resources that teach prompt design and operational controls can speed adoption; see the OECD overview of Law 31814 and this technical summary of Peru's AI regulation for details, or explore the AI Essentials for Work bootcamp syllabus and registration for practical workplace skills.

Program	Length	Early Bird Cost	Syllabus / Registration
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work syllabus and registration

Selection of the Top 10 was pragmatic and policy‑aware: entries were chosen where Peru's Law 31814's risk‑based, human‑oversight requirements meet operational value, following the playbook in the GSA's AI Guide for Government - start with a single, well‑scoped use case, assemble an Integrated Product Team, and prioritize mission alignment, data readiness and measurable KPIs (GSA AI Guide for Government).

Prioritization relied on simple, repeatable filters drawn from that guide (Impact / Effort / Fit), plus governance guardrails from leading frameworks so deployments avoid high‑risk biometric or social‑scoring pitfalls and embed transparency and accountability as default controls; see summaries of international standards and risk management approaches in the global governance overview (Global AI Governance: Five Key Frameworks Explained).

Practically, the methodology favored prompts and use cases that tie directly to citizen services, fraud detection or rural health triage - concrete, testable pilots that map to Peru's centralized oversight and can scale with documented governance, tooling and procurement paths.

Citizen service chatbots - whether a municipal hotline or a national 24/7 assistant - can cut wait times, automate repetitive form‑filling, surface approved FAQs and even speed incident reporting, turning bureaucratic friction into fast, usable help that citizens trust; practical Peruvian pilots already show what works: M2SYS's conversational AI highlights guided form completion, round‑the‑clock FAQs and automated complaint capture to ease pressure on call centers, while WWF Peru's “Justina del Mar” demonstrates how a WhatsApp chatbot co‑designed with fishers and validated by government partners builds credibility and reduces corruption risk by putting plain‑language rules and reporting channels in users' hands.

Design choices matter: keep bots grounded in verified databases, offer clear handoffs to human advisors, and pick channels people actually use (WhatsApp proved decisive for artisanal fishers).

For agencies scaling pilots, the evidence and guidance in industry reviews - like Optasy's analysis of government chatbot best practices - stress accuracy, privacy, accessibility and strong oversight so bots expand access without excluding those offline or vulnerable.

In the information age, ensuring that knowledge is not only accessible, but also understandable to all, is crucial for effective and equitable governance. This accessibility not only strengthens the autonomy and ability of our fishers to make informed decisions, but also fosters a community that is more empowered and aware of its rights and responsibilities.

Social program fraud in Peru can be tackled not by guesswork but with proven anomaly‑detection playbooks that blend machine learning, graph analytics and clear operating rules: industry guides show common schemes (promotion abuse, account takeovers and rings that exploit overlapping phone numbers or IDs) and even cite real losses - one college scammer cohort banked $30,000 through repeated small transactions - so the stakes are concrete and measurable (Fraud Anomaly Detection Program Guide for Government).

Practical steps for Peruvian ministries include standing up specialized fraud analytics teams, using error budgets and site‑reliability practices to allow safe experimentation, and codifying incident lifecycles so investigators and service teams act consistently; these process controls reduce false positives that otherwise create citizen friction.

Technically, fraud graphs and graph databases excel at surfacing linked accounts or “rings” (three accounts sharing contact details is a classic red flag) and feed ML models that prioritize cases for human review (Detecting Anomalies with Fraud Graphs and Graph Databases).

As national coordination accelerates compliant pilots, aligning analytics to audit‑ready standards helps Peru spot anomalies before whistleblowers or outside investigators do, turning data into defensible prevention rather than reactive damage control (National AI Program Coordination for Peru Government).

AI‑assisted tele‑triage can be a practical lifeline for Peru's rural clinics if built on proven playbooks: keep triage pathways simple and patient‑friendly as recommended in ACEP's quick tele‑triage guide, define clear staff roles and device requirements from telemedicine toolkits, and design workflows that escalate red‑flag findings to in‑person care when needed (ACEP Quick Tele‑Triage Guide (American College of Emergency Physicians), Telemedicine for Health Equity Toolkit - Care Innovations).

In rural settings the payoff is tangible: tele‑emergency programs have shown they can avoid costly transfers (one study estimated about $3,800 saved per patient who avoided transfer), reduce travel burdens and widen specialist access - but success depends on realistic planning for broadband, local staffing and clinician supervision as described in rural telehealth guidance.

Pairing these clinical controls with national AI program coordination and lifecycle controls helps Peru scale pilots that are audit‑ready and citizen‑centred (AI Essentials for Work syllabus - National AI Program coordination).

The result: faster risk‑stratification at the point of contact, fewer unnecessary transfers, and a rural clinic that can triage with the confidence of a hospital team watching over its shoulder.

“Telemedicine fosters a collaboration that reduces the feelings of isolation that physicians may experience when they go to practice in a small town.”

Personalized learning for K–12 in Peru can translate regional promise into classroom practice if pilots face reality: linguistic diversity, patchy broadband and scarce professional development demand solutions that are low‑bandwidth, bilingual and teacher‑centered, not just flashy apps.

Adaptive systems that surface gaps and offer targeted interventions - tools already discussed in coverage of AI in education - can reduce teacher workload by handling routine differentiation while freeing instructors to coach higher‑order skills; in practice that means investing in teacher training, curriculum alignment and inclusive content (including indigenous languages) so technology augments equity rather than widening divides.

Policy coordination matters too: national AI program coordination can help scale audited pilots, share resources and set procurement standards so districts buy tools that actually fit Peruvian classrooms.

For practical guidance on regional challenges and design principles, see the Edmentum review of personalized learning in Latin America and reporting on AI in education for how tools streamline admin and surface student insights.

"The term personalized learning has become ubiquitous. It is sometimes used interchangeably with related concepts such as adaptive learning, competency-based learning, and differentiated instruction.... This definition builds off the definition in the National Education Technology Plan (NETP) (U.S. Department of Education, 2010), as well as the Department's supplemental priorities and definitions for discretionary grant programs (U.S. Department of Education, 2014, 79 FR 73425). Objectives refers to the particular learning goals a student is working toward; pathways refer to the learning activities, tools, presentation modalities, and resources the student is using to attain these objectives; and pace relates to the amount of time the student needs to attain mastery of these objectives.... Personalization thus involves tailoring multiple elements of instruction, stressing the importance of understanding each learner as an individual, and matching learning experiences to his or her needs and interests. Technology is a critical tool for enabling these processes."

Peru's Law 31814 set the policy compass - promoting AI for economic and social development while demanding a risk‑based, human‑centred approach - and current drafting work is turning those principles into enforceable rules that will shape every public‑sector pilot.

The law embeds transparency, human oversight and data‑governance duties and places coordinated oversight with the Presidency of the Council of Ministers and the Secretariat of Government and Digital Transformation, so agencies can't treat AI as a quick add‑on; see the OECD overview of Peru Law 31814 for the core goals and institutional framing.

More detailed obligations are emerging in the SGTD's draft regulation, which pushes proportional privacy protections across an AI system's lifecycle and requires providers to disclose operation details, decision parameters and liability mechanisms, effectively insisting on

audit‑ready

AI (the draft's timeline and transparency rules are summarized in the SGTD policy note on AI draft regulation).

Parallel congressional proposals further tighten the design and certification regime - borrowing the EU's risk categories and prohibitions - so implementers should expect verification, reporting duties and potential sanctions while also gaining regulatory certainty that can attract investment and scale public‑sector solutions; see the Access Partnership analysis of draft bills.

The practical takeaway: treat compliance like a safety checklist - document models, log decisions and plan for certification - think of it as installing a flight recorder for public AI systems so services can innovate without losing citizens' trust.

Policy Element	What It Means for Government
Risk‑based regime	Classifies AI as unacceptable, high, medium or low risk; dictates controls and prohibitions
Enforcement authority	Presidency of the Council of Ministers / SGTD leads coordination and oversight
Key obligations	Transparency, human oversight, data minimization, incident reporting and possible certification

Fusing AI social listening with official data sources turns scattered signals into actionable crisis intelligence for Peru: real‑time sentiment and anomaly detection on social platforms can flag emerging issues while linking those spikes to internal incident reports, weather feeds or hospital triage logs so responders know whether a rumour is local noise or a true emergency.

Tools that read images and multiple languages help catch viral posts and memes that can inflame a situation - imagine a single photo of a flooded market shared thousands of times being auto‑triaged for verification - so communications teams can send targeted, culturally tuned alerts and counter‑misinformation quickly.

Industry guides show the pattern: fast filtering and predictive models reduce overload, NLP crafts audience‑specific messages, and dashboards convert chatter into escalation tickets for human teams to review (see NISM overview of AI for social media monitoring and crisis management and Personos analysis of AI for real-time crisis communication).

Success depends on clear thresholds, documented handoffs to human decision‑makers, and privacy‑forward controls so monitoring remains compliant and trustworthy; for practical alignment with national programs, integrate listening workflows with the National AI Program coordination playbook to keep pilots audit‑ready and scalable.

Tool	Key Strength
NISM: AI for social media monitoring and crisis management	Real‑time alerts and predictive spotting
Personos: AI for real-time crisis communication and misinformation detection	Scenario modeling, tailored messaging and misinformation detection
Convin: social listening tools for contact-center fusion	Multichannel sentiment fusion into agent workflows

“AI is only as good as the data it learns from. Bias, misinformation, and blind reliance on automation can create serious risks. Organizations need clear guidelines on how AI models are trained, tested, and used. Regular audits, diverse data sources, and clear accountability help ensure AI is ethical and reliable.”

Procurement & RFP automation can turn Peru's painfully manual sourcing cycles into auditable, faster workflows that still meet public‑sector scrutiny: AI can auto‑draft solicitations from approved templates, pull compliance clauses and certifications into a single knowledge hub, auto‑score proposals against weighted criteria, and flag expired docs or risky vendors so reviewers focus on strategy rather than paperwork.

Real government pilots abroad show the payoff - an AI RFP agent cut a typical 100‑question response from several hours to about 20–30 minutes and lifted win rates substantially - proof that time saved on boilerplate buys room for better evaluations and citizen‑centred requirements (see the Inventive AI case study).

Tools described by procurement vendors also offer supplier intelligence, dynamic dashboards and real‑time auctions to boost competition while keeping an audit trail for regulators (see GEP's overview of AI‑powered RFP software).

And adopting Chile's procurement playbook - focus on the public problem, multidisciplinary teams and data quality - helps Peru buy AI responsibly rather than simply automating old mistakes (see lessons from Chile's AI procurement experiments).

Start with low‑risk pilots, lock in legal and audit requirements up front, and treat the RFP repository as a civic asset that grows more valuable with each compliant use.

If projects are planned with these best practices from the outset, they can contribute to public value and adequately address privacy risks, biases, and lack of transparency. With ethical and responsible use of data, public buyers will help build trust with citizens and harness the potential of data to improve the design and management of public policies.

Automated minutes and legislative records can modernize Peru's committee rooms by turning spoken debate into searchable, timestamped transcripts, clear action items and compact summaries that respect Law 31814's audit‑ready and human‑oversight requirements; platforms with strong Spanish support and speaker recognition - like Fireflies meeting transcription with multi-language speaker recognition - make it realistic to capture plenary debates and committee testimony without losing attribution or context.

Lightweight tools that integrate with Google Meet, Zoom and Teams and produce instant recaps and follow‑ups - such as Tactiq live transcripts and ChatGPT meeting summarization workflow - help legislative staff publish timely records, speed publishing of minutes for transparency, and build centralized, queryable archives for oversight bodies and citizens alike.

Success depends on policy choices already in play - store transcripts with provenance, keep human review in the loop, and align retention and access controls with national AI program coordination so automated minutes become a civic asset, not a liability.

“This app is extremely useful. We love the summarizing ability. Makes sure that all meeting participants have a record of what was discussed and agreed to.”

Biometric systems sit at the sharp end of Peru's AI rulebook: Law 31814 and its implementing Regulations make real‑time identification in public places a banned or tightly circumscribed use and classify biometric categorization as high‑risk, so any agency considering facial recognition or emotion‑reading must plan for human oversight, traceability, and audit‑ready documentation rather than ad‑hoc pilots; see Peru's AI law overview for the core risk framework and the recent approval of Supreme Decree No.

115‑2025‑PCM which enshrines these controls in the national regulation. Practical risk review means treating biometric projects as high‑assurance engineering problems: require independent validation and documentation of data provenance, minimize and justify any sensitive data use, and design clear handoffs so humans can halt or correct automated outputs - precisely the lifecycle controls SGTD and the Presidency of the Council of Ministers are charged with enforcing.

The policy signal is unmistakable: imagine a packed Lima plaza being scanned by cameras - Peruvian rules largely close that door and instead nudge implementers toward narrowly scoped, consented identity checks, offline alternatives, or audit‑friendly verification workflows that protect rights while still enabling secure services; agencies should therefore prioritize non‑invasive options, strict data minimization and multi‑stakeholder oversight as they pilot identity tools under the National AI Program playbook.

Regulatory Point	What It Requires
Peru AI Law 31814 risk framework - AI regulation in Peru	Proportionality, human oversight, transparency, data governance, prohibition of some biometric uses
Supreme Decree No. 115-2025-PCM - AI regulations and SGTD oversight in Peru	Formal Regulations: labelling, traceability, SGTD oversight and phased public‑sector rollout

real-time biometric surveillance in public spaces (except under narrowly defined exceptions)

Peru's public agencies should treat social media as an active sensor and a two‑way channel - not a megaphone - and pair clear engagement rules with disciplined social listening so signals become usable intelligence; practical best practices like

engage with your audience

(reply, avoid ignoring negative feedback, and assign clear roles) set the tone for trusted channels (government social media best practices guide), while OSINT and monitoring playbooks show how real‑time collection, anomaly detection and predictive scoring turn chatter into actionable alerts for crisis teams or policy units (social media monitoring for government expert guide).

For Peru specifically, integrate these listening workflows with the National AI Program coordination so dashboards, archiving and access controls meet audit‑ready and privacy requirements and so rapid responses (a single viral post flagged and routed to the right human team) become institutionalized rather than ad hoc (National AI Program coordination).

Train analysts, set thresholds for escalation, and keep human review at the centre - this turns noisy platforms into a public‑service early‑warning system that builds trust instead of eroding it.

Peru's path from pilot to national scale must pair Law 31814's risk‑based guardrails with clear programmatic steps: start with audited, low‑risk pilots, keep human oversight and provenance logs at every handoff, and embed continuous monitoring so deployments evolve with emerging challenges - exactly the adaptive approach laid out in Peru's AI regulation overview (Peru AI regulation overview).

Regional cooperation amplifies capacity-building and evidence‑based policy, so align national playbooks with partners and APEC‑backed best practices to share lessons and avoid reinventing costly mistakes (APEC AI governance and cooperation).

Practical next steps for ministries are simple and concrete: inventory systems, set KPIs, fund independent audits, train civil servants in prompt design and lifecycle controls, and bootstrap skills with applied courses such as the AI Essentials for Work bootcamp syllabus and registration so teams can operate audit‑ready AI - think of each rollout as installing a flight‑recorder for public services: transparency that enables learning, trust and safer scaling.

Program	Length	Early Bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work syllabus and registration

“It becomes an imperative to look for dynamic ways to build this evidence-based policy makings and that requires investment into capacity and regional cooperation.”