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

France's drive to embed AI in government services is practical and policy‑led: a multi‑phase national strategy backed by billions has built research hubs and even expanded the Jean Zay supercomputer to massive capacity, giving public administrations real HPC muscle, while the Conseil d'État's seven principles for “trusted public AI” make human‑in‑the‑loop, transparency and frugality non‑negotiable for deployments - see the public‑service doctrine for details public-service doctrine on trusted public AI, and a useful synthesis of France's national AI strategy and infrastructure synthesis of France's national AI strategy and infrastructure.

That combination - clear doctrine, ministerial roadmaps and concrete compute - means chat assistants, predictive tools and fraud detection can save time while protecting rights; civil servants can gain the exact prompt‑writing and governance skills needed in short, applied courses like the AI Essentials for Work bootcamp (AI Essentials for Work bootcamp registration), turning policy into day‑to‑day service improvements.

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AI Essentials for Work	15 weeks; Learn prompts, AI at work foundations, and job‑based practical AI skills; Early bird $3,582; Register for AI Essentials for Work bootcamp

“AI for Humanity”

Selection of the Top 10 use cases and the accompanying prompt templates followed a policy‑and‑practice lens rooted in France's national AI trajectory: priority sectors (health, transport, environment, defence) and large public investment in research and HPC shaped what matters most for government deployers, while legal and ethical constraints under the EU AI Act and GDPR set hard guardrails for templates.

Key criteria were sector relevance to the “AI for Humanity” roadmap, regulatory fit (high‑risk rules and transparency obligations), technical feasibility from Jean Zay‑scale compute down to low‑carbon hosting, and operational frugality so civil servants can run safe assistants without massive energy bills.

Prompt templates were therefore designed with human‑in‑the‑loop checkpoints, provenance prompts to surface data sources, privacy‑minimising instructions that map to CNIL/GDPR guidance, and two flavours of templates - HPC‑tuned prompts for large models and streamlined, energy‑efficient prompts for edge or low‑carbon data‑centre deployments - so a forecasting model that can use Jean Zay's expanded capacity (see the Pulaski France AI analysis) can also degrade gracefully for local use.

Practical validation included compliance checks against AI Act timelines and public procurement realities described in France's legal practice guides, plus usability testing aimed at clear, auditable outputs for busy public servants (Pulaski analysis on France AI leadership, Chambers 2025 France AI practice guide, Business France artificial intelligence sector overview).

“The battle for artificial intelligence, and for technology in general, is nothing less than a battle for talent.”

EDF's grid stress forecasting work during summer heatwaves shows how AI moves from lab to life: transformer‑level forecasting that combines transformers and graph neural networks now simulates thermal stress and issues 72‑hour alerts, drives GIS overlays of high‑risk zones and even proposes demand‑shedding actions (smart appliances, industrial shifts and battery timing), cutting blackout risk by 74% and shortening operator response to under 3 minutes while reducing household outages 38% - measures that helped prevent three rolling blackouts in Rhône‑Alpes in summer 2024 (DigitalDefynd case study on EDF's heatwave forecasting).

Coupling that capability with short‑term energy prediction best practices - better battery charge/discharge scheduling, reduced curtailment and real‑time stabilisation - makes the system resilient as renewables swell on the grid (DNV's guide on short‑term forecasting explains these operational gains).

EDF's R&D also pairs technical forecasting with citizen‑facing awareness - an energy‑flexibility simulator that helps communities visualise load‑shifting benefits - so demand‑response becomes both actionable and understandable for operators and the public alike.

Predictive models for ICU demand and triage can be made both more accurate and more privacy‑respectful by training across hospitals without moving patient records: a recent federated‑learning study using a 5,000‑stay subset of the eICU database simulated three hospital silos and showed a global model that beat individual hospital models while keeping raw EHRs local, a critical point for GDPR‑governed systems in France - data never leaves the bedside, only model updates do (BioRxiv preprint: Federated Learning for ICU Mortality Prediction).

For French public hospitals, that pattern supports safe ICU bed‑allocation signals, triage support that preserves patient confidentiality, and mortality‑risk scores whose feature importances (age, heart rate, creatinine, SpO2) align with clinical intuition; the NStarX technical note summarises the simulated setup and outcomes for teams assessing privacy‑first deployments (NStarX technical paper: Federated Learning for ICU Mortality Prediction).

The striking takeaway: near‑centralized performance without centralizing data - so hospitals can share intelligence, not raw records, when seconds matter in the ICU.

Metric	Value
ICU stays (simulated)	5,000 across 3 hospitals
Federated model accuracy	79%
Federated model AUC	0.82

Capgemini's "AI for Justice" work maps clear, practical routes for French courts to use NLP, predictive analytics and automation to cut backlogs and make services more accessible: NLP can extract facts from unstructured dossiers, e‑filing and automated case creation speed routine workflows, and a Western European ministry pilot using ~12,000 past decisions showed how models can give judges fast, auditable estimates of compensation - freeing attention for the hardest cases.

Estimates that 69% of legal‑assistant tasks and 16–21% of judges' work could be automated underscore the "so what": routine triage can be scaled without replacing human judgment, while anomaly detection (Capgemini's Haystack) and analytics have been shown to boost investigations - one US district office saw human trafficking probes rise from 30 to 300 yearly after AI‑driven analytics.

For France, these gains must come with human‑in‑the‑loop safeguards and traceability; see Capgemini's AI for Justice point of view for design principles and the Nucamp guide on human‑in‑the‑loop process redesign for practical rollout steps.

“while we should strive for increased efficiency, we should also keep in mind the essential aspect of justice in the eyes of citizens: They rely on trust and confidence in the functioning of the system.”

SNCF's predictive‑maintenance push turns rolling stock into a continuous stream of operational intelligence so trains rarely “go dark” on the timetable: onboard SIMs, IoT sensors and LoRaWAN gateways feed algorithms that analyse up to 8,000 variables per train (2,000 in real time) and monitor thousands of sets to predict faults days ahead, halve technical incidents on equipped trainsets and free up capacity - for example, Paris Nord's Francilien fleet shrank from nine offline sets to six or seven, allowing an extra rush‑hour departure - so commuters feel the benefit as fewer delays and smoother peak service; read SNCF's detailed overview on how analytics and remote diagnostics work on the network in the SNCF predictive maintenance and remote diagnostics overview and the practical IoT rollout using LoRaWAN in the Actility LoRaWAN IoT rollout case study.

Beyond fewer breakdowns, the same sensor data and short‑term occupancy forecasts (Transilien challenges) feed smarter fleet scheduling and real‑time dispatch choices, meaning maintenance windows become surgical rather than calendar‑driven and operators can keep more trains in circulation without surprise disruptions.

Metric	Value
Variables analysed per train	8,000 (≈2,000 in real time)
Trains with SIMs/IoT sensors	1,100+ (remote diagnostics on 2,500 tracked)
Diagnostics accuracy	95% for some faults
Technical problem reduction	≈50%+ on instrumented trainsets
Cost & visit reductions	Maintenance costs −20%, shunting/visits −30%

“Relax. We're monitoring your train in real time.”

Territorial AI adoption in France is less about flashy pilots in Paris and more about pragmatic, privacy‑first rollouts that let towns reap real benefits without huge bills or data risks: the IJFMR review of “AI‑Driven Smart Cities in France” highlights how local municipalities must balance technological integration, economic limits, data privacy, the digital divide and public acceptance when scaling AI for urban planning, sustainability and public safety (IJFMR research paper: AI‑Driven Smart Cities in France).

Practical pathways include testbeds and sandboxes - and governance alignment - so that a regional council can trial a lightweight, energy‑efficient traffic or leak‑detection model under clear rules rather than deploying costly monoliths; lessons from the Paris AI Action Summit stress linking city projects to national governance to avoid fragmented standards (Aligning Urban AI and Global AI Governance - Paris AI Action Summit insights (The GovLab)).

That local‑first, frugal approach dovetails with France's “AI for Humanity” strategy and calls for robust policy frameworks, public‑private partnerships and continuous monitoring to ensure inclusion and accountability - concrete steps that make municipal AI practical, auditable and trusted across diverse French territories (France AI Strategy Report - AI Watch (European Commission)).

Urban water operators in France can leap from slow, complaint‑driven repairs to proactive, prioritized dispatch by pairing hydrophone‑based acoustic sensing with machine learning: controlled experiments show that integrating domain features into a Feature‑Informed CNN yields the best localization and detection performance (90.4% accuracy) versus traditional Random Forests (81.2%) or plain CNNs (82.9%), making real‑time flagging of suspected leaks across complex pipe geometries feasible - think crews guided to the single riskiest valve instead of chasing rumors across a whole neighbourhood.

That approach, described in a comparative SSRN study on hydrophone acoustic sensing and ML SSRN study: hydrophone-based acoustic sensing and machine learning for pipeline leak detection, complements earlier work on fast, pressure‑based detection in distribution networks Springer JIPR article: rapid and accurate leak detection in water supply networks, and fits France's push for frugal, low‑carbon deployments - local inference or energy‑efficient hosting can keep costs down and speed up alerts (low-carbon data centres and GPU capacity targets report).

The net result for municipalities: less water loss, fewer emergency digs, and dispatch lists that prioritise the leaks most likely to flood streets or damage infrastructure.

Model	Average Accuracy
Random Forest (feature‑based)	81.2%
Standard CNN (raw signals)	82.9%
Feature‑Informed CNN (FI‑CNN)	90.4%

Physics‑guided machine learning now gives planners a tool that is both interpretable and robust - models that pair turbine‑level geometric inputs with physics‑based wake efficiencies can predict wind‑farm output across unseen layouts, outperforming standard empirical wake models and helping quantify realistic yield and wake‑losses during permitting reviews (PRX Energy study: physics‑guided machine learning for wind‑farm power prediction).

For TotalEnergies‑style renewable site planning in France, that means environmental assessments can move beyond single‑number estimates to spatially explicit power maps (think seeing each turbine's “wake shadow” on a planner's map), reducing the risk of over‑optimistic capacity claims and sharpening impact mitigation measures.

Embedding human‑in‑the‑loop checkpoints keeps assessments auditable and aligned with public‑sector traceability expectations (Human‑in‑the‑Loop process redesign for government traceability and audits), while hosting models in energy‑aware infrastructure supports France's low‑carbon commitments and makes on‑site or regional inference practical (Low‑carbon data centres and GPU targets for regional AI inference in France), so permitting, impact analysis and site selection become faster, more defensible and easier to communicate to local stakeholders.

For France's public services, Orange is turning long‑range research into practical resilience: its Paris O‑RAN Integration Centre and RIC experiments aim to break vendor lock‑in and let operators automate priority flows, while the Pikeo standalone 5G blueprint in Lannion already proves software‑defined, edge‑first networks that pair Open RAN with AI‑driven automation - exactly the ingredients needed to keep emergency comms up when traffic spikes or disasters hit (Orange O‑RAN Integration Centre in Paris, Orange 6G white paper and Pikeo standalone 5G blueprint).

Coupled with Orange's Autonomous Networks practice - self‑healing, predictive maintenance and real‑time reprogramming - network slices and RIC xApps can dynamically steer capacity to first responders, reduce pointless field trips and keep critical services running under stress, while design choices around edge inference and energy efficiency answer France's low‑carbon and security priorities (Orange Autonomous Networks practice guide).

The tangible payoff is simple: more reliable emergency links and fewer human hours lost to avoidable outages - so citizens and crisis teams actually get the connection they need when it matters most.

“We are now using artificial intelligence to make Orange a better company across the board.”

Public‑sector AI buying in France will live or die by procurement design: transparent, auditable rules that reward energy‑efficient, privacy‑safe solutions rather than opaque vendor lock‑in.

A surprisingly concrete risk to watch for is boilerplate language that quietly favours trade‑group members - procurement notices have even asked bidders to be

Voting Members

of a standards body, a practice the SNIA has explicitly repudiated (SNIA procurement policy statement), so RFIs and tender texts must be carefully drafted.

Best practice checklists from modern procurement guides - prioritising fairness, clear vendor evaluation, and real‑time spend visibility - translate neatly to AI buying where ecosystem scoring (Ecoscore), traceability and human‑in‑the‑loop attestations are table stakes (Procurement governance – Complete Guide 2025).

International moves on exclusion and debarment rules show governments tightening the gate on risky suppliers, so French departments should pair robust vetting with incentives for low‑carbon inference and frugal AI rollouts; embedding simple, auditable checkpoints and redesigning processes to keep humans in control is a practical first step (Human‑in‑the‑Loop process redesign).

The

so what

is immediate: a cleaner RFP and a clear Ecoscore will steer tens of millions in public spend toward models that are secure, explainable and cheaper to run for years to come.

The 3IA network - MIAI@Grenoble‑Alpes, 3IA Côte d'Azur, PRAIRIE (Paris) and ANITI (Toulouse) - is the spine of France's public AI talent and research push, pairing regional strengths (health, transport, environment, aerospace) with large‑scale compute such as the Jean Zay supercomputer expansion referenced in France's national AI analyses; that mix turns academic chairs and summer schools into operational capacity, with PAISS summer schools drawing hundreds (300 participants from 52 nationalities in 2021) and a pipeline of specialists ready for public‑sector deployments.

These interdisciplinary institutes also accelerate transfer to industry - start‑up programs, shared engineer pools and cluster funding have made 3IA sites magnets for partnerships and training - see the Inria summary of the four selected 3IA projects and the wider national context in the Pulaski review of France's AI leadership and Jean Zay supercomputer expansion for further detail.

Metric	Value
Total 3IA budget	€251.5M
Academic chairs	149
Academic researchers	563
Doctoral & post‑doc funding	454
Partner companies	168
People trained per year	13,678
Publications	1,471

“This labeling enables the Azur consortium to be a center of excellence in terms of research and world‑class training in AI and a major player in the growth model of its territory.”

Clear, practical safeguards are the fast track to trustworthy AI in France: adopt the EU's seven requirements - human agency and oversight, technical robustness, privacy and data governance, transparency, fairness, societal & environmental well‑being, and accountability - use the ALTAI assessment to operationalise them, and align procurement and governance to reward low‑carbon, auditable solutions rather than opaque vendor lock‑in (see the EU Ethics Guidelines for Trustworthy AI).

Legally, GDPR's human‑intervention rules and the oversight expectations in the AI Act mean any high‑risk public system must keep a human able to review, stop or reverse outcomes - think of a “stop button” or mandatory human review for benefit denials - so teams should embed meaningful human‑in‑the‑loop checkpoints from day one (read the practical summary on human intervention and oversight under GDPR and the AI Act).

Next steps for departments: run ALTAI self‑checks, pilot frugal inference in low‑carbon hosts, and build audit trails into procurement. Beginners can get started with short, applied training that teaches prompt design and governance basics - courses such as the AI Essentials for Work bootcamp make those skills immediately useful for civil‑service contexts, turning policy guardrails into day‑to‑day practice with concrete, auditable steps.

Requirement	One‑line summary
Human agency & oversight	Humans must be able to intervene, oversee and make informed decisions about AI outputs.
Technical robustness & safety	AI must be resilient, secure and have fallbacks to minimise unintentional harm.
Privacy & data governance	Respect privacy, ensure data quality and control access to personal data.
Transparency	Traceability and clear explanations adapted to stakeholders; users know they interact with AI.
Diversity, non‑discrimination & fairness	Avoid bias, involve stakeholders and ensure accessibility for all.
Societal & environmental well‑being	Design systems to benefit society and limit environmental impact.
Accountability	Put in place auditability, redress mechanisms and clear responsibility for outcomes.

“AI, which is going to be the most powerful technology and most powerful weapon of our time, must be built with security and safety in mind.” - Jen Easterly, Director CISA.