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

Norway is moving from pilot projects to national strategy: the Government's National Strategy for Artificial Intelligence spots clear strengths in health, the seas and oceans, public administration, energy and mobility and commits to data sharing, language resources and trustworthy AI - while new initiatives like the AI Norway national initiative and regulatory sandboxes aim to speed safe experimentation; a recent policy push even backed dedicated AI research funding to build capacity.

The public sector's emphasis on transparency, human rights and alignment with the EU AI Act means deployments will focus on explainable, well-governed use cases (predictive maintenance, clinical decision support, service chatbots) rather than unchecked automation, and practical upskilling matters: courses such as the AI Essentials for Work bootcamp (Nucamp registration) teach promptcraft and workplace AI skills for professionals who need to apply these tools responsibly.

For anyone tracking government AI, Norway combines high trust, strong registries and deliberate governance - and that mix makes it a promising testbed for responsible public‑sector AI.

Bootcamp	Length	Early bird cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register for AI Essentials for Work bootcamp (Nucamp)

“The Government is now making sure that Norway can exploit the opportunities afforded by the development and use of artificial intelligence, and we are on the same starting line as the rest of the EU.”

This list was compiled from Norway‑focused primary sources and expert syntheses: the Government's National Strategy for Artificial Intelligence provided the baseline on priorities (data sharing, language resources, sector focus and public‑sector pilots) and concrete examples such as Lånekassen's residence‑verification trial, which used machine learning on 25,000 cases and selected 15,000 by model with markedly better detection rates; cross‑checks were made against international comparators and summaries such as the Norway National Strategy for Artificial Intelligence (government report), the OECD AI policy dashboard for Norway, and the European Commission's country review (European Commission AI Watch country report for Norway) to capture infrastructure, governance and sandbox activity; legal and market signals were validated with practice guides and regulatory trackers to surface procurement, liability and implementation constraints.

Priority was given to official documents, regulator reports and peer‑reviewed policy summaries so the use cases reflect Norway's current pilots, policy levers and practical constraints rather than speculative scenarios.

Artificial intelligence will not only enable us to perform tasks in increasingly better ways; it will also enable us to perform them in completely new ways.

Digdir is steering the conversation about citizen‑facing automation toward human‑centric, trustworthy services - showcasing prototypes like the My Way assistant for life events and promoting the Nordic Trust Model as a governance backbone in its Nordic DigiGov Lab webinars (Digdir webinar on human-centric governance and AI-driven solutions).

On the ground, Norway's chatbot story balances scale and care: municipal pilots and regional platforms such as Kommune‑Kari demonstrate broad reach, while NAV's virtual agent Frida handled a pandemic surge - answering some 270,000 inquiries and, at peak, absorbing traffic equivalent to the work of 220 full‑time staff on a single Friday - freeing humans for complex cases (boost.ai case study on NAV Frida virtual agent handling pandemic inquiries).

Practical deployments in Oslo show another pattern: chatbots coupled with RPA and microservices can standardize responses and trigger back‑end actions, improving speed without losing oversight (Capgemini case study on Oslo municipality chatbot and RPA integration).

Together these examples underline a clear lesson for public‑sector teams: design for hybrid human/AI workflows, instrument transparency, and measure service impact - not just novelty.

Program	Reported metric
Kommune‑Kari	Surpassed 500,000 conversations/year; >1 million messages in 2020
NAV – Frida	Answered ~270,000 inquiries during pandemic spike; Friday traffic ≈ work of 220 FTEs (80% resolution)

Norway's immigration rules are famously specific - so much so that a simple eligibility checklist makes a perfect target for administrative decision‑support: Utlendingsdirektoratet (UDI) requires applicants for permanent residence aged 18–67 to document at least NOK 325,400 in earned income over the last twelve months and to show that income came from accepted sources (employment, business income, pensions, certain benefits, etc.) UDI requirement to financially support yourself for permanent residence, while EU/EEA rules add another layer about registration, the right to reside and what counts as proof UDI FAQ on EU/EEA registration and right to reside.

Those clear rules and hard cutoffs - plus the separate citizenship test structure (36 questions; 24 correct to pass; A2 language level) Norwegian citizenship test details and passing criteria - mean practical tools like automated triage, document‑completeness checks, eligibility calculators and targeted reminders could reduce rejections and speed cases without changing legal judgment: the system would flag missing payslips or non‑accepted income types, prompt applicants to wait to apply until they meet the NOK 325,400 threshold, and surface edge cases where UDI explicitly cannot confirm a right to reside - turning dense regulation into actionable steps for applicants and caseworkers alike.

NAV's sick‑leave caseload is a textbook use case for predictive triage: Norway's rich, linked registries let researchers use group‑based multi‑trajectory modelling to split long‑term sick‑listed workers with depression into four clear paths (for example,

GP 12 weeks

and

GP & MED 12 weeks

), and those patterns predict sustainable return‑to‑work (SRTW) outcomes and gendered differences - trajectories with sustained GP follow‑up (longer consults or talking therapy over 12 weeks) showed the highest likelihood of SRTW, while short GP contact or specialist‑heavy pathways were linked to lower odds for some groups (e.g., Specialist+GP+MED had a 19–23% lower likelihood of SRTW for women) (see the register study).

That means predictive analytics fed by registry markers (visit cadence, medication dispensings, specialist referrals) can realistically flag cases early that would benefit from longer GP support, targeted talking therapy or expedited coordination - turning dense administrative records into actionable triage and helping direct scarce GP resources where they raise return‑to‑work chances most.

The finding is stark: about 70.3% of the cohort did not attain zero sick‑leave days during follow‑up, so smarter triage could materially change outcomes and costs in Norway's welfare system; for the original analysis, see the BMC study on depression care trajectories, and for practical AI-in-government framing see Nucamp AI Essentials for Work bootcamp syllabus and AI pilots in Norwegian public services.

Trajectory	Share of sample
GP 12 weeks	32.7%
GP 2 weeks	18.6%
GP & MED 12 weeks	40.0%
Specialist, GP & MED 12 weeks	8.7%

Statens pensjonskasse (SPK) sits at the intersection of scale and sensitivity: as Norway's main public occupational pension manager for more than 1.1 million current and former employees and roughly NOK 647 billion in administered pensions, it has both a clear need for automated fraud detection and a strict obligation to limit and govern personal data.

Practical AI use cases for SPK centre on anomaly detection and payment‑integrity scoring - flagging outlier disbursements, missing or inconsistent income records, and automated completeness checks - while operational security and deletion procedures must keep pace, a lesson underlined when the Norwegian Supervisory Authority fined SPK NOK 1 million after unnecessary income data on about 24,000 disability pension recipients was retained (EDPB decision summary on SPK data retention).

At the same time SPK is strengthening defenses: an eight‑year partnership with cybersecurity provider mnemonic delivers 24/7 managed detection and response and incident handling to protect member data and support safe AI operations.

Combining robust MDR, minimal‑data principles and well‑instrumented anomaly models lets SPK improve payment integrity without repeating past data‑management errors - because at pension scale, a single oversight can mean thousands of affected members and major regulatory consequences (and vigilance must be visible to keep public trust).

Metric	Value
Members / beneficiaries	~1.1 million
Assets administered	NOK 647 billion
GDPR fine / affected individuals	NOK 1,000,000; ~24,000 individuals
Cybersecurity contract	8‑year MDR & 24/7 incident handling with mnemonic

“We're noticing that this requires more of us all. In addition, the IT world is quite different now than earlier. New technological developments and the use of cloud provides us with less control and new challenges. As we're developing and adapting to new realities, we see the importance of also becoming a professional receiver of security services. This way, we're making sure we do our part to secure the large amounts of data we manage for our members, and get the most out of this partnership with mnemonic.”

For details, review the EDPB decision summary on SPK data retention and the SPK–mnemonic 8-year MDR partnership announcement.

Helsedirektoratet faces a clear, data‑driven mandate: diagnostic imaging in Norway is enormous and uneven, and the consequences matter for quality, access and cost - one national analysis counted 37,871,276 imaging examinations from 2013–2021 (about 4.2M per year) with 2021 averaging 0.79 exams per inhabitant and dramatic regional gaps (Oslo University Hospital Trust performed nearly twice the imaging per person as Finnmark) (BMC study on temporal and geographical variations in diagnostic imaging); a companion qualitative study then surfaces the human and system drivers behind low‑value imaging that undermine equity and efficiency (BMC study on drivers for low‑value imaging).

Together these findings frame practical priorities for national clinical decision‑support: tighten referral criteria, target supply‑sensitive incentives in high‑use urban centres and strengthen guidance where private imaging fills public wait lists - because when one trust can be doing nearly twice as many scans per resident as another, small policy and workflow changes could meaningfully reduce unnecessary exams and improve equal access to high‑value diagnostics.

Metric	Value
Total imaging examinations (2013–2021)	37,871,276
Average per year	4,207,920
Average exams per inhabitant (2021)	0.79
Outpatient share	71%
Outpatient imaging at private centres	32%
South‑East vs West (per inhabitant)	53.1% more in South‑East
Oslo vs Finnmark (per inhabitant)	~96% more in Oslo (nearly double)

For Sjøfartsdirektoratet, Norway's real-world foothold in autonomous shipping - exemplified by the electric Yara Birkeland quietly running a 67‑km coastal route - turns global promises into urgent regulatory work: machine learning can squeeze fuel and emissions from voyages by analysing past trips, weather and traffic to optimise routing and decision‑making, while advanced sensor suites (LiDAR, radar, cameras, IMUs) and resilient satellite links are needed to keep vessels safe and observable at sea (SailorSpeaks: Autonomous Ships - Reshaping Maritime Transportation (2024)).

The payoff is tangible - studies cited in the literature point to big efficiency and safety gains, plus dramatic emission cuts when optimisation is paired with cleaner propulsion - but the knotty problems are governance, COLREGs adaptation, liability and cybersecurity.

Practical next steps for Norwegian authorities include championing phased rollouts from controlled short‑sea routes, investing in port automation and traffic‑management integration, and tightening data‑security and procurement language so public tenders lock in minimal‑data, auditable models (see practical procurement guidance in the Nucamp AI Essentials for Work syllabus - procurement guidance).

If Sjøfartsdirektoratet gets the framework right, autonomous vessels can move from novelty to a cost‑saving, low‑emission backbone for coastal logistics - but only with clear rules, strong comms and visible risk controls.

Statnett is using sensors, image analytics and richer market telemetry to shift Norway's transmission network from calendar‑based checks to condition‑driven, predictive maintenance: minute‑by‑minute production and flow data feeds real‑time models that spot hot transformers, stress on lines and frequency deviations around the 50 Hz operating band before they cascade into outages, while dynamic line rating and targeted monitoring have already unlocked up to a 20% increase in line capacity in pilot work.

Visual inspection scaling is part of the story too - partnering on advanced image analysis to process roughly 40,000 high‑voltage tower inspections a year turns mountains of photos into prioritized work orders - and data platforms that contextualize SCADA, weather and hydrological inputs compress analysis time and deliver measurable savings.

Together these pieces - real‑time power system data, automated inspections and a unified industrial data platform - let Statnett customise maintenance windows, reduce outages and speed connection decisions, moving the grid toward safer, cheaper and more flexible operation for a renewables‑heavy Norway.

See Statnett's live data and system overview and the Scopito inspection programme for concrete examples of this approach.

Metric	Value
High‑voltage tower inspections / year	~40,000
Increase in line capacity (pilot)	Up to 20%
Estimated annual savings (Cognite partnership)	$2 million
Faster time for connection analysis	60% faster

“We're developing digital solutions and making them available to our core business, which will help us boost our efficiency, improve our customer response time, and reduce costs.”

Procurement is where policy meets practice: Norway's public buyers purchase more than NOK 500 billion of goods and services every year, and the Ministry of Trade and Industry is pushing that spending power toward innovation - encouraging “innovation partnerships” that let public agencies co‑develop AI solutions with suppliers so startups can compete more fairly and early R&D gets embedded into contracts (see the National Strategy's procurement chapter Norway National Strategy for Artificial Intelligence).

That also raises contract workstreams that matter in practice: clear IP/ownership clauses, performance metrics for model accuracy, liability drafting aligned with Norwegian tort and product rules, and express plans for future regulatory changes (including the AI Act) are all non‑negotiable.

Legal teams and vendors increasingly turn to specialised counsel and legal‑tech for boilerplate that isn't just legalese - clauses must operationalise audit logs, deletion rules and explainability requirements so a single contract won't quietly lock away public data or bar reuse for other agencies.

For practical contracting help and AI‑procurement checklists, Norway's leading technology law practices explain how to bridge technical specs and legal safeguards with commercial reality (Wikborg Rein – Artificial Intelligence practice).

The upshot: smart procurement can turn public demand into scalable, auditable AI - or, if done poorly, into years of costly rework and public‑trust risk.

Metric	Note
Annual public procurement	More than NOK 500 billion (public sector)
Procurement instruments	Innovation partnerships, guidance on IP/ownership and model contractual clauses

NorwAI, hosted at NTNU in Trondheim, is Norway's flagship SFI for research‑based AI innovation and a practical anchor for safe AI development - organizing workstreams like TRUST, DATA, LAP (language & personalization) and HYB (hybrid AI analytics) and linking three universities, research institutes and industry partners to push models from prototype to audited deployment; recent activity includes a public launch of six new Norwegian models and deep domain projects in health and maritime that feed directly into national priorities (NorwAI research centre at NTNU, AI for Ocean and Health - key areas for Norway (NTNU)).

That practical focus also shows up in NTNU's public‑sector pilots - working with the Norwegian Data Protection Authority to test Copilot‑style tools and build a “Copilot‑ready” toolbox for privacy, consent and operational controls (NTNU Copilot pilot testing AI tool for public sector).

For Norwegian agencies the payoff is concrete: NorwAI's mix of anonymisation research, synthetic‑data methods and industry partnerships makes it possible to train and validate models on realistic but non‑identifiable datasets - so public servants can trial decision‑support that behaves like the real thing without risking registry leaks, a capability that helped partners prototype fraud detection and even saved banks large sums in live pilots.

Metric	Value
Awarded (Research Council)	NOK 96.0 million
Project period	2020–2028
Consortium	3 universities, 2 research institutes, 11 companies

“The generative AI wave provides us with new and efficient ways to work.”

Datatilsynet has made one thing unavoidably clear for public‑sector AI in Norway: high‑risk systems need hard compliance work up front, not after the model ships.

Its sandbox reviews stress that processing with “innovative” AI, large‑scale profiling, employee monitoring or health data typically triggers a mandatory Data Protection Impact Assessment (DPIA), and controllers - not vendors - carry the legal duty to run it; see the Datatilsynet DPIA guidance for practical checkpoints and red flags.

Sandboxes such as Helse Bergen's show why: fairness and built‑in protection (Article 25) must be designed into models, with ongoing quality assurance to catch drift, false positives/negatives and demographic distortion, and clear rules so outputs remain decision‑support rather than fully automated decisions (Article 22 risks).

For procurement and operations that involve smaller buyers, Datatilsynet flags a difficult asymmetry - SMEs often lack the in‑house expertise to complete robust DPIAs - so practical moves are publishing DPIAs, keeping privacy policies in sync, specifying threshold values and explicitly dividing responsibilities between supplier and controller to make audits, transparency and remediation straightforward in practice (read the Datatilsynet DPIA guidance and the Helse Bergen sandbox report for examples and recommended measures).

“The sandbox therefore concluded that the use of Secure Practice's tool requires a DPIA. It is the responsibility of the controller to ensure that a DPIA is conducted.”

Ready for practical first steps in Norway's public sector? Start small and governed: use KI‑Norge and the AI sandbox as a reference point for safe experimentation and align early on with national oversight (see the Norwegian Government AI Norway (KI‑Norge) announcement at Norwegian Government AI Norway (KI‑Norge) announcement), but treat data protection as non‑negotiable - Datatilsynet expects a Data Protection Impact Assessment where generative or innovative AI is used, and controllers must own that DPIA process (Datatilsynet Data Protection Impact Assessment (DPIA) guidance).

Follow NTNU's playbook: pick one low‑risk pilot (meeting summaries or document triage are ideal), map what data is accessed, run a DPIA, restrict the scope, train users, monitor outputs, and phase rollouts; update DPIAs continuously as tools evolve.

For non‑technical staff, build practical prompt and governance skills - courses like Nucamp's Nucamp AI Essentials for Work bootcamp teach promptcraft, risk awareness and workplace deployment so teams can move from curiosity to compliant, measurable impact without tripping legal or ethical traps.

“The Government is now making sure that Norway can exploit the opportunities afforded by the development and use of artificial intelligence, and we are on the same starting line as the rest of the EU.”