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

Switzerland's federal and cantonal governments are shifting from cautious pilots to practical AI that can ease pressure on health services, social security and transport - areas Deloitte flags as high-potential - while grappling with governance, data protection and even legacy systems (yes, some care providers still use fax) Deloitte overview of AI in the Swiss public sector.

The Federal Council's 2020 guidelines, canton-level sandboxes and the national debate over a light-touch, sectoral approach (plus the decision to endorse the Council of Europe AI Convention) mean Swiss administrations are designing AI with trust and legal alignment in mind; White & Case's AI Watch summarizes that regulatory trajectory and timing White & Case AI Watch regulatory tracker for Switzerland.

For public servants and managers ready to apply AI responsibly, practical training - like an essentials course on prompts and workplace AI - bridges the skills gap and accelerates safe deployment Nucamp AI Essentials for Work bootcamp syllabus.

Bootcamp	Length	Early bird cost
AI Essentials for Work	15 Weeks	$3,582
Solo AI Tech Entrepreneur	30 Weeks	$4,776
Cybersecurity Fundamentals	15 Weeks	$2,124

“We are undoubtedly in an era of radical innovation and change and there is a mounting need for AI's fast and effective governance.” - Alois Zwinggi

Methodology blended international benchmarking with Swiss policy reality: each candidate use case was scored for public‑sector readiness using the Oxford Insights Government AI Readiness Index 2024 - which assesses 188 governments across 40 indicators in three pillars (Government, Technology Sector, Data & Infrastructure) - to flag what's immediately deployable versus what needs more capacity-building.

Scores were then checked against national priorities set out by SERI and the Federal Administration - education, research and innovation, cross‑administrative data sharing, plus the 37 priority action areas from the interdepartmental working group - to ensure alignment with Switzerland's bottom‑up, technology‑neutral approach and recently published guidelines (SERI guidelines on artificial intelligence in the ERI sector).

Practical filters included legal/ethical fit, data and telecom infrastructure needs, workforce reskilling potential, and canton-scale feasibility; each use case had to pass a triple‑lens check (policy, tech, data) so the list favors solutions that can move from pilot to production with trust - not just promise - which matters when public services are at stake.

Switzerland's move to sign the Council of Europe's Framework Convention on Artificial Intelligence on 27 March 2025 marks a pragmatic turn in policy & regulatory drafting: rather than a single, sweeping “Swiss AI Act,” the Federal Council has chosen a plural, rights‑focused route that uses the Convention as the legal backbone while favouring sector‑specific amendments and non‑binding measures to keep innovation alive and practical implementation flexible (BAKOM overview of Switzerland's approach).

The next concrete milestone is a consultation draft from the FDJP, DETEC and FDFA by end‑2026 to translate the Convention into measures on transparency, data protection, non‑discrimination and supervision - but parliamentary approval (and even a possible referendum) means some obligations may not land before 2030, so public bodies and vendors should treat the period ahead as a regulatory transition window.

For lawyers and risk teams the signal is clear: expect stronger accountability, disclosure duties and sector‑tailored rules - a change that's less about banning technology and more about making AI auditable, explainable and compatible with Swiss constitutional rights and economic freedom (Council of Europe press release, White & Case AI Watch).

“A machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations or decisions that may influence physical or virtual environments.”

Cantonal immigration offices can bring a sharply practical AI moment to citizen services by modelling a secure, self-service digital contact centre like Ireland's Digital Contact Centre (DCC), which guides users to log in, book or amend first-time appointments, check application status and submit tightly categorised queries so issues reach the right team quickly - for example the Irish form even routes My IRP card has been lost or stolen straight to the specialist desk Irish Digital Contact Centre - submitting renewal queries.

In a Swiss canton this same approach - paired with multilingual chatbots for routing, quick FAQ replies and document-upload prompts - reduces back-and-forth, frees caseworkers for complex judgements, and aligns with national thinking about safe public-sector AI in 2025; see how routine claims triage and FAQ bots reshape frontline roles in government services Claims triage and FAQ chatbots in government services and how the broader Swiss Federal AI strategy will change service design Swiss Federal AI Strategy 2025 - government service design.

Query Sub‑Category	Query Topic (example)
First Time Registrations	I am having difficulty booking an appointment
Online Renewal Applications	I wish to add documents to or update my application
Irish Residence Permit (IRP) Cards	My IRP card has been lost or stolen
Re‑Entry Visas	I would like to request an emergency re‑entry visa appointment

Legal research in Swiss public administration is becoming both faster and more evidence-driven: weekly case-roundups like the TaxLawBlog summaries of Federal Administrative Court tax rulings show the granular, topic-by-topic flow of decisions (VAT and international tax recur), while machine-readable repositories such as the Swiss Federal Supreme Court Dataset (SCD) let teams search and train summarisation pipelines across 116,650+ judgments updated quarterly - a level of scale that turns a courtroom archive into structured inputs for prompt-driven assistants TaxLawBlog weekly tax rulings summaries and the UZH collection of legal datasets UZH Swiss Federal Supreme Court Dataset (SCD) database.

Practical constraints from judicial review doctrine - who can appeal, what counts as excess discretion, and typical timelines (the Federal Supreme Court averages about 185 days for public‑law remedies) - are usefully codified in guides such as the Lexology Q&A Lexology Q&A on judicial review in Switzerland, so summaries can highlight not just outcomes but reviewability and precedent risk, making case digests genuinely actionable for policy teams and counsel.

Metric	Value
SCD (Swiss Federal Supreme Court Dataset)	116,650+ cases (updated quarterly)
Federal Supreme Court decisions (IPI, 2023)	23 decisions
Average time - Federal Supreme Court (public law remedy)	185 days

Clear, accurate multilingual publishing is a core operational challenge for the Federal Office of Public Health (FOPH): charged with national health policy and running dashboards and registries, the FOPH must publish the same guidance across seven social channels - Bluesky, Facebook, Instagram, LinkedIn, TikTok, X and YouTube - while respecting data‑use and information‑security rules set out on its guidelines pages FOPH social media and data-use guidelines and the organisation's public site Federal Office of Public Health (FOPH) official website.

Switzerland's four national languages (German, French, Italian and Romansh) and the need for certified documents for official procedures mean translation is not just convenience but compliance - commercial providers even advertise fast, certified delivery tailored to Swiss authorities, including the FOPH certified translation services for Swiss authorities in Switzerland.

In practice that creates a tiny logistical opera: every bulletin, infographic or epidemiological update must clear content‑policy checks, privacy scrutiny and multilingual certification before it reaches cantonal health officers or the public, so publishing workflows and vendor contracts need to bake in both speed and auditability.

Aspect	Detail
FOPH social channels	Bluesky, Facebook, Instagram, LinkedIn, TikTok, X, YouTube
Official languages	German, French, Italian, Romansh
Digital health programme	DigiSanté (national digital transformation initiative)

Cantonal hospitals can turn existing administrative and EMR feeds into practical capacity‑planning and triage prediction tools that nudge bed managers and clinicians before a ward becomes gridlocked: Swiss research recommends tracking

unjustified stays

as an early warning of inappropriate hospitalisations (BMC 2022 study measuring medically unjustified hospitalizations in Switzerland), while longitudinal work shows daily care demand is driven by capacity utilisation, patient turnover and clinical complexity - exactly the signals a short‑term predictive model needs (JMIR 2021 study on variation of daily care demand in Swiss general hospitals).

Recent machine‑learning work in Switzerland has even produced a decision‑support tool to predict delayed discharges using anonymised patient identifiers and demographic/admission features, which is the kind of data input that makes real‑time dashboards actionable for coordinators and discharge teams (BMC 2025 decision-support tool to predict delayed discharges in Swiss hospitals).

The takeaway is straightforward and tangible: a well‑trained model that flags a rising length‑of‑stay risk and a parallel uptick in nursing‑sensitive outcomes can turn yesterday's paper pile into a colour‑coded ward map that stops bottlenecks before they cascade into ambulance diversions or overnight boarding.

Study	Key data/metric
Measuring medically unjustified hospitalizations (BMC, 2022)	Recommends unjustified stays indicators to monitor inappropriate hospitalisations
Variation of Daily Care Demand (JMIR, 2021)	Analyzed capacity utilisation, patient turnover, clinical complexity
NSO prediction models (SMW secondary analysis)	36,149 hospitalisations; model AUCs ~0.75–0.84 (training/test)

Switzerland's State Secretariat for Economic Affairs (SECO) - the federal centre of expertise for economic policy - is uniquely placed to shepherd AI decision‑support that speeds social‑security triage and reemployment routing while keeping a focus on outcomes and governance; SECO already commissions employment and training programmes and even backs outcome‑based projects abroad, so the institutional know‑how for measuring

"what works"

is on hand (SECO INDIGO profile - State Secretariat for Economic Affairs Switzerland).

Practical implementations start small: claims‑triage assistants and multilingual FAQ chatbots can automate routine eligibility checks, flag high‑priority cases, and surface tailored referrals to reemployment services - reducing backlogs and freeing caseworkers for complex decisions - exactly the pattern signalled by analyses of claims triage risk in government services (Analysis of claims triage and FAQ chatbots in government services).

Picture a colour‑coded dashboard that routes a jobseeker to a targeted training pathway before their unemployment spell exceeds a critical threshold: it's low‑friction for users, auditable for policymakers, and measurably focused on employment outcomes - an operational sweet spot for SECO's remit and contracting experience.

Organisation	Role	Policy areas / Example projects
State Secretariat for Economic Affairs (SECO)	Swiss federal economic policy centre / commissioner	Employment & training - Cali Progresses with Employment; Empleando Futuro; CREO (workforce development projects)

For the Canton of Zurich, digital‑twin simulations tied to agent‑based models offer a practical bridge from research to street‑level traffic management: ETH Zurich's prototype that integrates MATSim shows how a common simulation baseline can feed repeatable, scenario‑driven experiments for public planners (ETH Zurich MATSim agent-based transport modeling prototype), while the CSFM symposium highlighted that the hard part is not the models but stitching together live sensors, vehicle flows and administrative data so the twin reflects reality rather than a stale snapshot (CSFM symposium on data integration challenges for digital twins (ETH Zurich)).

The recent European Transport Research Review survey of digital twins reinforces that these platforms are most valuable when used for policy testing, demand forecasting and what‑if corridor simulations - picture a canton dashboard that turns corridors from green to red before congestion arrives, giving operators time to reroute buses or tweak signal timing (European Transport Research Review: review of digital twins for transport planning).

Switzerland's new Ordinance on Automated Driving (OAD) creates a pragmatic, canton‑friendly path for autonomous mobility: since 1 March 2025 the rules permit highway pilots that let drivers release the steering wheel under readiness‑to‑resume control (Level 3), automated parking in designated, signposted areas, and driverless vehicles on canton‑approved routes provided they're monitored by a qualified remote operator and meet FEDRO's safety guidance - so pilots, last‑mile shuttles and freight runs can scale under explicit supervision (Swiss Ordinance on Automated Driving (OAD) - federal legislation, Pestalozzi analysis: Self-driving cars - the new reality in Switzerland).

The OAD also tightens operational duties - type approval, driving‑mode memory that timestamps emergencies (with GNSS coordinates), training and record‑keeping for vendors and operators - and keeps civil liability largely on vehicle owners, so fleets must pair innovation with rigorous documentation.

Think of it as a regulated testbed: legal clarity and a literal

black box

for autonomy let cantons experiment without sacrificing public‑safety traceability.

Use case	Automation level	Key operational requirement
Highway pilot	Level 3	Driver must be ready to retake control
Automated parking	Level 4	Designated/signposted parking areas; equipment & data logging
Driverless vehicles (shuttles/last mile)	Level 4–5 (use‑case dependent)	Cantonal route approval + remote operator monitoring

Fraud detection and anomaly‑spotting in Switzerland's public finances is becoming both a whistleblower and a data science problem: the Swiss Federal Audit Office (SFAO) relies on a public whistleblowing channel where citizens and federal staff can report suspected irregularities (SFAO whistleblowing reporting offices), and its audits can uncover major losses - the RUAG MRO review flagged possible fraud and inventory gaps and

several tens of millions in damage

that demand forensic follow‑up (RUAG MRO audit (Swissinfo)).

At the same time, procurement digitisation and large federal spending pools (CHF 6.39 billion above WTO thresholds in 2023) mean procurement datasets are fertile ground for anomaly detection, and recent systematic reviews map how data‑driven methods can detect procurement fraud at scale (study on detecting fraud in public procurement (EPJ Data Science)).

The pragmatic takeaway for Swiss administrations is to couple SFAO's reporting and audit powers with reproducible, auditable analytics so a single suspicious line item can be flagged, investigated and traced back to evidence before errors cascade into reputational or fiscal loss.

Item	Detail / source
Whistleblowing portal	Public reporting channel for private individuals and federal employees (EDA reporting offices)
RUAG MRO audit	Auditors flagged possible fraud, inventory irregularities and tens of millions CHF in damage (Swissinfo, Feb 2025)
Procurement data & digitisation	CHF 6.39 billion procured above WTO threshold in 2023; HBB rollout in 2023 creates datasets for anomaly detection (SFAO publications)

Infrastructure condition monitoring at the Swiss Federal Roads Office (FEDRO) is a perfect match for computer‑vision inspections: open research datasets such as the CODEBRIM concrete‑defect bridge image collection provide labelled, multi‑target, multi‑class images and bounding boxes that speed model training and reproducible evaluation - the Zenodo record documents original images, cropped patches and balanced classification sets totalling dozens of gigabytes of annotated bridge photos, plus code to reproduce the CVPR study CODEBRIM concrete-defect bridge image dataset on Zenodo.

For Swiss road managers the practical upside is clear: models trained on curated datasets can turn periodic photo surveys into continuous, auditable risk-ranking so engineers focus on the structures that need hands-on repair first, aligning with the service-design changes flagged in the broader Swiss Federal AI Strategy 2025 guide for government AI in Switzerland - in short, tens of gigabytes of labelled images can translate into earlier, cheaper fixes and fewer emergency lane closures.

File / Dataset	Size
CODEBRIM_classification_balanced_dataset.zip	12.2 GB
CODEBRIM_original_images.zip	8.3 GB
CODEBRIM_classification_dataset.zip	7.9 GB

Switzerland's path to useful, trustworthy public‑sector AI is now clearer: a national, quality‑first push - visible in Swiss {ai} Weeks and the new Swiss LLMs - puts transparency, multilinguality and public engagement at the centre of adoption (Swiss AI Weeks official site); but cautionary evidence from enterprise practice shows most pilots stall unless tied to clear outcomes and frontline adoption - “95% of projects never make it past pilots” is a useful alarm bell when planning scale‑up (BankInfoSecurity analysis: Most AI pilots never take flight).

The pragmatic recipe for cantons and federal agencies is simple: pick high‑value, low‑risk first bets (capacity planning, chatbots, document translation), mandate auditable governance, and invest in staff who can translate policy into prompts and processes - practical training such as an Nucamp AI Essentials for Work syllabus helps bridge that skills gap and keeps deployments measurable and auditable.

Start small, measure impact, and use open, local models and clear contracts to avoid vendor lock‑in; the payoff is public services that work smarter and remain accountable - think a canton dashboard that flips from green to red before the morning rush, not after.

Bootcamp	Length	Early bird cost
AI Essentials for Work (registration)	15 Weeks	$3,582
Solo AI Tech Entrepreneur (registration)	30 Weeks	$4,776
Cybersecurity Fundamentals (registration)	15 Weeks	$2,124

“Artificial intelligence belongs in the middle of society.”