The Complete Guide to Using AI in the Government Industry in Chile in 2025

AI matters for Chile in 2025 because a clear national roadmap - the Chilean AI Policy 2021–2030 led by the Ministry of Science - ties together enabling factors, adoption and ethics to empower citizens and prepare the workforce for disruption; the policy is publicly documented as a national strategy (Chilean AI Policy 2021–2030 national strategy).

Practical pilots and R&D are already being catalogued - like the State's Superintendency of the Environment's initiative listing AI use cases in government (AI use cases in the public sector by Chile's Superintendency of the Environment) - while regional trends push risk‑based, human‑centered regulation.

With Chile's IT sector flagged as high‑potential, the so‑what is immediate: simple automation can free staff for higher‑value work, so reskilling is urgent. For public servants and leaders, workplace-focused courses such as the AI Essentials for Work bootcamp - practical AI skills for the workplace offer practical skills to apply AI safely and productively across agencies.

Chile's stance in 2025 is proactive and pragmatic: the Chilean AI Policy 2021–2030 - led by the Ministry of Science, Technology, Knowledge and Innovation - frames AI around three practical pillars (enabling factors; development and adoption; and ethics, regulation and socio‑economic impacts) and sets an action plan with monitoring to move public and private actors toward shared standards (Chilean AI Policy 2021–2030 - OECD dashboard).

At the same time, Chile has advanced from strategy to legislation: a draft AI Bill introduced in May 2024 adopts an EU‑style, risk‑based approach that would create an AI commission, a national registry and pre‑authorisation for high‑risk systems while banning certain unacceptable uses such as remote biometric ID in public spaces except in narrowly defined cases - steps that could raise enforcement and capacity questions for regulators and agencies alike (Chile's AI Bill analysis - pioneering policy facing local limits).

The bottom line for Chilean government teams is clear: governance, risk management and human‑centred safeguards are no longer optional - preparing compliant procurement, documentation and oversight processes will be the difference between safe pilots and costly rollbacks.

Chile in 2025 is where infrastructure, policy and investment meet to power real AI adoption: ranked first in the ILIA 2024 index and already the third‑largest data‑centre market in Latin America, the country hosts roughly 58 data centres with about 150 MW of colocation capacity and a National Data Center Plan that projects US$2.5 billion and 28 new facilities - tangible signals that cloud and AI workloads are being planned at scale (InvestChile report: Chile leader in artificial intelligence in Latin America).

Capacity keeps rising fast - reports show another ~100 MW of power added by end of 2025 and an eventual build‑out that could total ~250 MW - so Santiago's metro region increasingly functions like a low‑latency hub connecting Latin America to global clouds and Asia via new submarine links (GlobeNewswire: Chile Data Center Portfolio Report 2025).

The payoff for government agencies is concrete: reliable, local compute capacity and investment (including dedicated AI research funding) reduce latency, improve data sovereignty, and make it realistic to pilot high‑value AI services - imagine powering document automation and predictive maintenance without routing sensitive records across continents, or turning on what amounts to a small city's worth of compute for a national model training run.

“Talking about expansion means talking about new capabilities, and Latin America has been standing out globally as a new technology hub for data center development and operations. The Chilean market, like the Brazilian one, is Ascenty's main focus at this moment, as our clients increasingly demand service capacity in the Latin American region,” says Marcos Siqueira, VP of Strategy and CRO at Ascenty.

Chile's government is already running concrete AI pilots that show both promise and the governance headaches that come with them: the Social Security and Medical Insurance Agency (SUSESO) has contracted two AI projects - a gradient‑boosting medical‑claims model to speed decisions and a classification‑tree audit for occupational mental‑health claims - while roughly 200,000 claims flowed through the agency last year and some 20,000 still waited for decisions, making automation tempting but risky (detailed SUSESO case study at the World Privacy Forum).

To keep those systems fair and transparent, Chile has fostered homegrown governance tools - GobLab's Ethical, Responsible and Transparent Algorithms project developed an Algorithmic Transparency Report Card and statistical bias measurements now used in procurement guidance - while ChileCompra's Standard Bidding Terms (now under revision) exposed a key tension: traditional procurement still weights price and competition heavily, even as agencies add bias, explainability and data‑protection criteria (more on GobLab's tools and public procurement guidance).

At the same time, national studies show the upside: a Stanford‑sponsored analysis estimates roughly 31% of public‑sector tasks could be accelerated with Generative AI, pointing to immediate “quick wins” (data entry, templated responses, reports) that free staff for higher‑value work if paired with robust oversight (Stanford analysis on Generative AI's impact on work in Chile).

The practical lesson is clear: pilot high‑value automations, require vendor documentation and audits, and design human‑in‑the‑loop workflows so faster decisions don't mean unfair ones.

“Success might be defined another way; in situations affecting people's wellbeing and livelihoods, use of a well‑designed and assessed model in support of speedier‑yet‑thoughtful human decisions can constitute success.”

Generative AI is not a distant possibility for Chile in 2025 but a practical lever for public‑sector productivity: rigorous studies show that roughly 4.7 million workers - nearly half the workforce covered by the 100 most common occupations - could accelerate more than 30% of their tasks, a theoretical value equal to about 12% of national GDP, and the public sector alone could unlock over $1.1 billion a year by streamlining admin‑heavy work (Stanford report: Impact of Generative AI on Work in Chile).

The evidence points to concrete “quick wins” for agencies: automate data entry, templated responses and report generation first, target education where 65–75% of teachers' tasks are accelerable (more than $1.2 billion in potential gains), and support SMEs (44% of tasks optimizable) with tailored tools and training so benefits aren't limited to large firms (SSRN paper: Generative AI impact on Chile's workforce).

The policy implication is simple and urgent: pairing pilots with worker training, procurement that demands vendor documentation, and human‑in‑the‑loop workflows turns theoretical GDP gains into safer, equitable improvements in service delivery - think of reclaiming bureaucratic hours across ministries and redirecting them to citizen‑facing work that actually requires human judgment.

“GenAI can speed up routine tasks and work alongside humans, allowing people to focus on higher‑value work.”

Quick wins in Chile are concrete and tightly scoped: start pilots in claims processing and consumer‑facing admin (build on the SUSESO medical‑claims project case study to require vendor documentation and human‑in‑the‑loop audits), automate invoice/PO triage and templated responses in back‑office finance and BPO functions, and layer AI into industrial maintenance and procurement where Lean value‑stream mapping and AI error‑proofing pay off fast - see practical methods in the Lean + AI error‑proofing and value‑stream mapping write‑up.

A low‑risk path is an AI Opportunity Audit followed by a 30–60‑day pilot that measures before/after metrics, keeps approvals in the loop, and protects sensitive data; pilots wrapped this way avoid the common trap that sinks so many projects.

Expect modest but real gains (Staple AI notes examples like two hours saved per employee monthly in operations) and move from one or two reliable pilots to scale with a trusted partner who can bridge POC to production - because, as the evidence warns, execution not models is the main barrier to impact.

“The core issue is not the quality of AI models, but the ‘learning gap' for both tools and organizations.”

Agentic AI offers a practical blueprint for service redesign in Chile: treat citizen journeys as a single, intelligent “front door” that guides users through complex, cross‑agency tasks and actually completes steps on their behalf.

Platforms like Thoughtworks' Seamless Government Experience (SGX) show how agents can pre‑check eligibility, route referrals and keep a case moving behind the scenes - imagine a flood survivor using one portal to apply for temporary housing, food aid and emergency funds without chasing three different agencies (Thoughtworks Seamless Government Experience case study on transforming government services with AI).

In practice, Chilean teams should combine agentic orchestration with deterministic automation: UiPath's agentic automation examples pair RPA precision with AI agents, adding a Trust Layer and Maestro orchestration to monitor models, KPIs and safe scaling across government systems (UiPath agentic automation and GovInsider analysis of RPA to AI agentic automation).

Crucially, data governance‑by‑design is the foundation - embed provenance, access controls and audit trails up front so agents can act reliably across legacy silos and protect citizen data (Capgemini on data governance‑by‑design for scaling agentic AI in government).

Start with a thin‑slice MVP for one high‑friction life event, keep humans in the loop for decisions that affect rights, and measure outcomes so the “no wrong door” promise becomes a dependable reality rather than a marketing line.

“Governments are starting to look at themselves more as an economic balance sheet. If taxes are your revenue and citizens are your customers, you'll start thinking about customer experience a bit more.”

Governance, ethics and procurement are now front‑and‑centre for Chilean agencies adopting AI: the draft AI Bill introduced in May 2024 borrows an EU‑style, risk‑based framework that mandates transparency, documentation and human oversight while proposing new institutions - yet experts warn Chile may lack the enforcement capacity and clear liability rules to make those mandates work in practice Analysis of Chile draft AI Bill (May 2024).

That national ambition sits on the Chilean AI Policy 2021–2030 pillars - enabling factors, development & adoption, and ethics/regulation - so agencies should expect standards to be tightened and monitored as the policy and law converge Chile AI Policy 2021–2030 details.

Practical compliance will mean treating AI as an auditable asset: classify risks, keep exhaustive model and data documentation, embed meaningful human‑in‑the‑loop checks, and require vendor attestations for high‑risk systems (testing, validation and provenance are now procurement essentials, per recent guidance on Chile's AI requirements) Chile AI compliance and transparency guidance.

so what?

The Bill could prohibit certain uses and create oversight bodies (including a proposed Data Protection authority) whose powers remain vague - so procurement teams should bake enforceable audit clauses, liability allocation and capacity‑building into contracts now to avoid costly stop‑gaps later.

An operational roadmap for Chilean agencies should be practical and phased: 1) convene a public‑private advisory group early - replicating Chile's Business Advisory Group approach from the national Business Action Plan on Biodiversity - to build trust and shared mission across ministries and industry (World Economic Forum: Chile Business Action Plan on Biodiversity); 2) prioritise measurable, high‑value use cases (health waiting‑list reduction and referral optimization are proven candidates from IDB pilots) and map baseline KPIs (Inter-American Development Bank report on shortening waiting lists in Chilean health pilots); 3) adopt a staged testing vocabulary - proof‑of‑concept, prototype, pilot, MVP - and define exit criteria and timelines for each stage so investments are time‑boxed and results are comparable (Nesta guide to proof-of-concept, prototype, pilot and MVP terminology); 4) lock governance and procurement guards in before scaling: require vendor documentation, data‑sharing agreements and clear accountability; 5) start locally with municipal or sector thin‑slice pilots (leverage existing municipal technical assistance channels used by GCoM in Chile to fast‑track deployment); 6) embed evaluation partners from academia and research - Chile's pandemic vaccine rollout shows how government‑academy partnerships speed implementation and legitimize results - and publish lessons; and 7) plan scaling around measured wins and capacity building so successful pilots translate into funded programs rather than one‑off demos.

The practical payoff is immediate: well‑structured pilots, clear KPIs and trusted partnerships turn speculative AI projects into services that shorten waits, free staff for judgment work, and pass the public‑trust test.

“Mainstreaming biodiversity across sectors has not been without its challenges,” says Maisa Rojas Corrad, Chile's Minister of Environment.

Chile stands at a decisive moment: with a May 2024 draft AI Bill proposing an EU‑style, risk‑based framework and a proposed AI Technical Advisory Council, the country can choose to turn regulation into a competitive advantage - or into a compliance bottleneck if capacity gaps are ignored (Chile's AI Bill analysis and local limits).

The practical next steps are clear and urgent for agencies: treat AI as an auditable asset (classify systems, require vendor attestations, and bake liability and audit clauses into procurement), invest in regulatory sandboxes and academia partnerships to test real workflows, and scale workforce readiness so human‑in‑the‑loop safeguards are meaningful rather than symbolic.

Regional reviews note that multistakeholder governance, sandboxes and human‑rights‑centred principles are now common best practice across LatAm - Chile should lean into those mechanisms to avoid becoming an enforcement bottleneck while still protecting rights (AI regulation in Latin America: overview and emerging trends).

For civil servants and procurement teams looking for immediate, practical capacity building, workplace‑focused training - for example Nucamp's AI Essentials for Work - equips non‑technical staff with prompt‑writing, risk‑aware use cases and operational checks so pilots become safe, measurable services rather than expensive rollbacks (AI Essentials for Work bootcamp syllabus).

The call to action is simple: pair thoughtful regulation with fast, local capacity building and pilot‑first sandboxes so Chile's ambitious law protects citizens without stifling the real productivity gains public services need now.