The Complete Guide to Using AI in the Government Industry in Marshall Islands in 2025

The Marshall Islands in 2025 faces urgent, overlapping threats - sea level rise that drives coastal erosion and saltwater contamination of groundwater, fisheries disruption from warming oceans, and mounting heat- and storm-driven health risks - so AI matters because it helps scarce government resources work smarter and faster where they'll save lives and livelihoods.

AI can power coastal erosion hotspot ranking to prioritize low-cost defenses and community action, speed analysis of climate-health vulnerabilities for remote atolls, and automate routine finance and reporting to free staff for emergency planning; see the PIRCA climate assessment for the RMI for the scope of risks and the WHO/GCF brief on strengthening health-system resilience for vulnerable communities.

Practical training matters too: short, workplace-focused courses like the AI Essentials for Work bootcamp teach the prompt-writing and tool skills public-sector teams need to deploy these high-value, locally targeted AI applications while linking to existing adaptation priorities.

“This is a crisis today for children and we need partnerships, collective action across the board.” - Michael Newsome, UNICEF

The Marshall Islands' local context in 2025 is stark: low-lying atolls with an average elevation of about 2 metres are already feeling the double-speed tide of change, as studies note the pace of global sea level rise has roughly doubled since the early 1990s - making king tides, frequent flooding, coastal erosion and saltwater contamination of freshwater a daily governance challenge (see the Climate Knowledge Portal and the PIRCA RMI climate report for the full assessment).

That combination of chronic inundation and episodic storm surge is why targeted, data-driven planning matters: the USGS inundation exposure assessment for five representative islands (Aur, Tobal, Ebon, Likiep and Mejit) shows how high-resolution elevation data can pinpoint vulnerability hotspots for adaptation or managed retreat.

Ocean warming and coral loss also threaten fisheries and food security, while hotter days and stronger cyclones raise public-health risks and push migration pressures already seen in past Marshallese relocations.

The implication for government AI projects is practical and immediate - tools for rapid inundation mapping, groundwater salinity monitoring, and climate finance prioritization can help stretch scarce resources to protect communities before more land and livelihoods are lost.

“[It's] completely unfair. We shouldn't have to do that. These are extreme measures that will cost us billions of dollars, all because of something we had contributed nothing to.” - Kathy Jetn̄il‑Kijiner

In 2025 the Marshall Islands will see a cautious, practical turn toward AI: expect targeted pilots that turn satellite and elevation data into rapid inundation maps and coastal‑erosion hotspot rankings to steer scarce adaptation funds, speed groundwater salinity monitoring, and automate routine finance and reporting so staff can focus on on‑the‑ground resilience work - a pragmatic next step mirrored in global uptake trends like the Stanford AI Index's reported surge in adoption and investment.

But this will be a double‑edged sword: Norton Rose Fulbright's analysis warns that AI can amplify efficiency while magnifying bias, opacity and human‑rights risks in migration and asylum contexts, so projects must bake in representative data, meaningful human oversight, and clear accountability from day one.

Think of AI as a compass for adaptation dollars that can either point communities to safety or, if miscalibrated, steer help to the wrong atoll; the 2025 task is to scale what works fast while locking down legal and procedural safeguards.

Read more on practical coastal use cases via the coastal erosion hotspot ranking and the legal risk perspective from Norton Rose Fulbright and the Stanford index.

“AI won't supplant human judgement, accountability, and responsibility for decision-making; AI will augment it.” - Michael Outram

What counts as “regulated” in 2025 is largely being shaped beyond the Pacific: the EU's Artificial Intelligence Act has set a concrete, risk‑based playbook that other governments and procurement teams should track closely, because it is already being touted as a potential global standard.

The Act splits AI into unacceptable, high, limited and minimal‑risk tiers, creates specific obligations for high‑risk systems and for providers of general‑purpose AI (GPAI), and builds in incident reporting, documentation and human‑oversight rules that would matter for any government deploying AI in public services; see the EU AI Act high‑level summary for the core structure.

Crucially for small governments and local vendors, the text requires Member States to host at least one national AI regulatory sandbox (Article 57) to test systems in supervised, real‑world conditions by 2 August 2026, and the EU guidance includes SME‑friendly measures - priority, free access to sandboxes and simplified documentation - designed to lower the bar for smaller actors (read the Small Businesses' Guide).

Timelines and phased obligations (from bans on unacceptable systems to later GPAI and high‑risk deadlines) mean procurement plans should build in compliance checks, transparency clauses and human oversight from day one; think of the EU Act as a lighthouse that signals where regulatory shoals lie and where safe harbours - like sandboxes and clear SME guidance - exist for cautious experimentation.

High-value AI projects for the Marshall Islands in 2025 are intensely practical: use satellite and high‑resolution elevation data to automate rapid inundation and coastal‑erosion hotspot mapping that directs scarce adaptation dollars to the right atolls, pair drone and microtasking pipelines with machine learning to cheaply update building and shoreline maps, and deploy sensors plus simple AI models for real‑time groundwater salinity monitoring so freshwater risks are caught before wells become undrinkable; these approaches mirror the World Bank's playbook for digital tools and visual scenarios that help governments plan big adaptation moves for tiny, low‑lying islands.

AI can also streamline routine finance and reporting - freeing staff for community consultations - and support participatory scenario visualizations that make trade‑offs visible to elders who hold complex land‑tenure knowledge.

Pilot these as supervised, small‑scale sandboxes linked to clear human oversight and community rights safeguards, because migration and displacement are already realities for many Marshallese households.

One vivid way to picture the need: an island just 200 metres wide and two metres above sea level becomes far easier to plan for when AI turns layers of data into a clear map of safe zones and retreat options; see the World Bank's guide to digital resilience and the coastal erosion hotspot ranking for practical templates and prompts to get started.

“Being able to visualize not only current impacts but future scenarios and options for adaptation became a key component of the project to equip the government with a very powerful tool for its own consultation and planning process.” - Artessa Saldivar-Sali, Senior Municipal Engineer, World Bank

High‑risk AI uses Marshall Islands government teams should avoid or tightly regulate are those that privatize life‑and‑death choices under a veneer of “efficiency” - notably automated asylum or status‑determination tools, biometric surveillance and facial‑recognition at borders, predictive profiling that steers relocation or returns, and machine‑only translation or credibility scoring without trauma‑aware human review.

Legal analysis warns that AI in asylum systems can be opaque, biased against non‑English speakers and cultural minorities, and prone to “false positives” that risk refoulement or wrongful exclusion; see Norton Rose Fulbright's cautionary piece on AI in the refugee space.

Litigation and FOIA requests over USCIS's Asylum Text Analytics (ATA) show how even screening tools can escape public scrutiny and amplify disadvantages for unrepresented claimants, so any migration or protection use must demand transparency, audit logs, and accessible remedies.

Civil‑society critiques of the EU AI Act also flag dangerous loopholes that let migration and law‑enforcement uses operate with less oversight, a warning sign for small island states where a single misapplied system could decide the fate of an atoll community already squeezed by king tides and saltwater intrusion.

Practically: classify migration and border‑control systems as high‑risk, require human‑in‑the‑loop review, mandate representative training data and independent audits, and avoid automated deportation or allocation decisions altogether until robust safeguards and redress are in place.

“USCIS's increased reliance on AI-based tools risks jeopardizing asylum seekers' access to a life-saving legal status,” said Jessenia Class, clinical law student with the Harvard Immigration and Refugee Clinical Program.

Governance and legal guardrails for AI in the Marshall Islands should be pragmatic, risk‑based and tailored to small‑island capacity: mandate a public AI inventory and periodic risk assessments, bake human‑in‑the‑loop checks into any system that affects relocation, benefits or health services, require vendor disclosures and data‑protection clauses in procurement, and set clear incident‑reporting, audit and redress processes so errors can be traced and fixed quickly.

Practical toolkits already used elsewhere point the way - see GAN Integrity's primer on building comprehensive AI governance, state playbooks that require agency AI inventories and risk programs, and Optiv's three‑part security policy (acceptable use/data protection, AI risk management, and incident response) for concrete controls to adopt.

Start small with supervised sandboxes for coastal mapping and groundwater monitoring, pair automated outputs with community review, and fund recurring training and an ethics or oversight committee so staff on remote atolls can spot bias or drift; the goal is to unlock AI's operational gains without letting a single mis‑calibrated model divert scarce adaptation dollars from the atoll that needs them most.

“And compliance officers should take note. When our prosecutors assess a company's compliance program - as they do in all corporate resolutions - they consider how well the program mitigates the company's most significant risks. And for a growing number of businesses, that now includes the risk of misusing AI. That's why, going forward and wherever applicable, our prosecutors will assess a company's ability to manage AI-related risks as part of its overall compliance efforts.” - Lisa Monaco (quoted in GAN Integrity)

Make procurement practical and low‑risk by treating every AI pilot in the Marshall Islands as a staged experiment: start with clear problem statements and a modest budget, require a 3–6 month pilot/sandbox for coastal mapping or groundwater salinity monitoring, and add AI‑specific questions into your existing third‑party risk management so vendors disclose training data, governance and security controls (see the OneTrust AI vendor assessment checklist).

Insist contracts split work into discovery, pilot and scale phases, spell out data‑use and IP rules, build in testing and acceptance metrics, and require explainability, audit rights and an exit plan so small agencies aren't locked in (see the RPC procuring AI commercial checklist with contract language).

Don't forget people: upskill a multidisciplinary team, demand ongoing provider-led training and knowledge transfer, and pair automated outputs with community review and human‑in‑the‑loop checks.

Finally, document everything in a public AI inventory and use a simple procurement checklist to capture overlooked steps like incident reporting, compliance cost allocation, and model‑security measures - the Cybersecurity Law Report AI procurement checklist is a helpful practical companion for busy procurement officers.

This approach keeps AI pilots fast and nimble while protecting fragile atoll communities from opaque decisions that could cost more than the technology itself.

Start by building government AI literacy: enroll core teams in short, practical programs like the UNESCO AI Literacy train‑the‑trainer initiative and the InnovateUS “Responsible AI for the Public Sector” courses so local civil servants learn not just tools but the ethics, risks and procurement questions to ask; supplement that with ITU Academy's “Artificial intelligence for the Public Sector” to deepen governance and service‑design skills and to form a small cohort of certified Marshallese trainers who can spread skills across atoll administrations.

Next, turn learning into local capability by pairing trained staff with a focused, low‑risk pilot - ideally a coastal mapping or groundwater‑salinity dashboard - so teams practice vendor evaluation, data governance and human‑in‑the‑loop checks on a real problem the community cares about.

Prioritize train‑the‑trainer models and free, open courses (so costs don't block participation), document progress in a public AI inventory, and require simple procurement clauses that demand explainability and audit rights; the payoff is practical: a village leader on a 2‑metre‑high atoll can see, on a single color‑coded map, which few dry metres to defend and which homes need relocation planning next.

“Education is the most powerful weapon which you can use to change the world.” - Nelson Mandela

The Marshall Islands' AI trajectory in 2025 is pragmatic and urgent: with a new national legal backbone for digital services in the form of the Digital Transformation and Identity Verification Act 2025, the focus should be on tightly scoped, high‑impact pilots - coastal inundation mapping, groundwater salinity dashboards and finance automation - that pair local oversight with regional best practices and capacity building.

Regional analysis like the AI Asia Pacific Institute's State of AI in the Pacific Islands underscores both the promise (climate response, reduced isolation, cultural preservation) and the gaps (infrastructure, governance, digital literacy), so the sensible path is to combine legal readiness with hands‑on training and small sandboxes.

Short, workplace‑focused upskilling - such as the 15‑week AI Essentials for Work course (see the AI Essentials for Work syllabus) - can give civil servants the prompt, procurement and oversight skills to run safe pilots; simultaneously, RMI should pursue regional alignment on standards and interoperable toolkits to avoid reinventing costly controls.

The result: faster, cheaper adaptation decisions for atolls that can't afford mistakes, and a growing local workforce able to steward AI rather than be governed by it (linking practical pilots to legal guardrails in national law will be the difference between useful tools and risky guesswork).

“AI right now feels in some ways like the new Internet of Things.” - Raymond Kok