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

Mauritius has a clear reason to double down on government AI: the 2018 national roadmap targeted high‑impact sectors - agriculture, health, FinTech, transport, manufacturing and the ocean economy - but, as the OECD notes, it lacked a robust implementation plan and the recommended Mauritius Artificial Intelligence Council was never fully institutionalized; recent efforts via the Mauritius Emerging Technologies Council and on‑the‑ground pilots show the promise of moving from strategy to delivery (see the Mauritius AI Strategy (OECD) and a case study on smart irrigation and pilots at iAfrica).

With a thriving hub in Cybercity, practical projects such as AI‑driven irrigation covering roughly 40% of sugarcane farms highlight immediate wins, while focused upskilling is essential - short professional courses like Nucamp AI Essentials for Work (15-week bootcamp) can fast‑track the civil service from plans to citizen‑facing services and measurable efficiencies.

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

“Leaders need to be actively investing in understanding how AI can make a difference for their company today.”

Selection favoured use cases that are tightly anchored to the Mauritius AI Strategy's stated priorities - agriculture, health, FinTech, transport, manufacturing and the ocean economy - and that show clear paths from pilot to scale, measurable public benefit, and feasible governance arrangements; this approach mirrors the Strategy's sectoral focus and its call for proof‑of‑concept pilots to build momentum (see the OECD's Mauritius AI Strategy).

Weighting also accounted for evidence of multi‑stakeholder buy‑in (the Strategy was developed through working‑group sessions) and practical readiness: examples with short pilot horizons and demonstrable capacity to improve service delivery were scored higher, while areas requiring heavy new infrastructure were deprioritised.

Regional policy signals about ethical governance and the need for legislative catch‑up informed a separate governance score - Mauritius' regulatory position (including sectoral rules) and regional reviews were used to penalise high‑risk, low‑transparency options.

Finally, emphasis on workforce and skills building ensured chosen use cases could be sustained locally rather than outsourced, following the Strategy's recommendations for capacity development and coordinated implementation (see dig.watch analysis and the regional review by Paradigm Initiative).

Priority sectors (source: Mauritius AI Strategy)
Agriculture
Health
FinTech / Finance
Transport
Manufacturing
Ocean economy

“It is evident that without swift and comprehensive legislative action, the region risks falling behind in AI's ethical and effective governance, which has significant implications for human rights and societal well‑being.”

Citizen‑facing chatbots and multilingual virtual assistants are now a practical way for Mauritius to make government services truly inclusive: homegrown platforms like R.I.Y.A‑AIx Mauritius open-innovation AI platform promise an open, locally governed AI that speaks English, French and Mauritian Creole (and more) so a farmer, student or tourist can get help in the language they use every day, while targeted deployments such as Port Louis mental-health chatbot for Mauritian residents show immediate operational wins - 24/7 coverage, 90% shorter wait times and steep cost reductions in early rollouts - by combining Creole/French support with local regulatory guardrails.

Complementary translation and OCR work - from KreolTech's mobile tools to government efforts with global partners to add Creole into major platforms - make documents and signage machine‑readable and serviceable across languages, closing the digital language gap so a citizen can scan a public form or chat at midnight and get a culturally accurate answer instead of a generic response (Mauritian Creole AI translation and OCR initiatives).

The result: faster access, lower costs, and a more equitable digital public square where AI adapts to Mauritius' living languages rather than forcing citizens to adapt to the machine.

Platform / Initiative	Language support & note
R.I.Y.A‑AIx	English, French, Mauritian Creole (+ Hindi, Mandarin, Bhojpuri) - open innovation, locally hosted
Conferbot (Port Louis)	Creole/French/English - pre‑trained on Mauritian mental health regs; 24/7 service
AI translation / KreolTech & Google partnership	Focused Creole translation, OCR and mobile scanner apps to bridge document & signage gaps

“We didn't want an AI that simply replicated foreign models,” said the project's lead architect.

Automating document processing is a practical, high‑impact next step for Mauritius' National Archives and e‑services: Optical Character Recognition (OCR) and thoughtful capture strategies turn paper, scans and even microfilm into searchable, editable records so clerks and citizens aren't hunting through boxes or static PDFs - an approach shown to cut manual search time dramatically and to unlock analytics for transparency and policy work.

Best practices from global practitioners stress four essentials for a successful rollout: start with a clear scanning plan and roles, pick scanners and OCR that match document quality and volume, index intelligently (simple, complex or situational capture strategies), and bake in security, access controls and ongoing QA so accuracy and compliance scale with use (see practical OCR guidance from BMI Imaging's OCR guide for document management and implementation tips from ArtsylTech's OCR technology implementation guide).

The GAO's work on electronic records underscores the governance side: without strong lifecycle rules and audits, digitisation risks create more chaos than clarity, so pairing automated capture with records schedules and oversight is non‑negotiable.

For Mauritius this means pragmatic pilots that prove time and cost savings, plus training for records teams so searchable archives become a citizen service rather than a back‑office burden - imagine turning a decades‑old microfilm reel into a searchable page in minutes, not weeks.

Action	Why it matters
BMI Imaging OCR guide for document management and capture	Makes documents searchable and automates data extraction
ArtsylTech OCR technology and indexing & QC strategy guide	Matches accuracy needs to document types and volumes
National Security Archive OCR resource on governance & lifecycle rules	Ensures preservation, compliance and accountable access

For the Ministry of Health and Wellness in Mauritius, AI-driven appointment scheduling and predictive patient‑flow tools are a practical lever to sharpen access and free up scarce clinical time: research shows AI scheduling can cut no‑shows by up to 30%, shrink patient wait times dramatically, and halve routine admin work while offering 24/7 self‑service booking and dynamic re‑fills of cancelled slots so a freed appointment is filled within minutes (see Brainforge's analysis of AI appointment scheduling and Innovaccer's comparison of AI vs traditional scheduling).

Beyond front‑desk gains, predictive scheduling feeds surveillance and capacity planning - real‑time analytics highlight peak demand, guide staff rostering, and reduce overcrowding in emergency departments, turning historical bottlenecks into actionable signals for planners.

Pilot deployments can start small (a single hospital or clinic) and prioritize secure EHR integration, role‑based access and staff training so privacy and fairness are baked in; the practical impact is tangible: fewer empty slots, faster care for urgent cases, and measurable time saved for clinicians to focus on patients rather than paperwork.

Impact	Reported result (source)
No‑show reduction	Up to 30% (Brainforge / Prospyr)
Wait‑time reduction	Up to 80% in some workflows (Brainforge / Accenture cite)
Administrative time saved	Up to ~50% (Brainforge)
Always‑on booking	24/7 self‑scheduling via portals, apps, SMS (Innovaccer / Prospyr)

“Everybody is trying to get to online scheduling, and Hyro is the fast track. They allowed us to open online scheduling for patients with confidence, keeping providers happy by ensuring that only accurate appointments are booked.”

Mauritius can tighten the financial front line with AI systems that pair real‑time transaction monitoring and behavioural analytics to stop fraud before it costs citizens and the state: machine‑learning platforms detect anomalies in milliseconds and enable instant actions - declines, freezes or stepped‑up authentication - so a suspicious inbound payment never becomes a loss (see Nuvei's primer on Nuvei real‑time fraud monitoring primer).

Sandboxed pilots run with the Ministry of Finance or under Bank of Mauritius supervision should prioritise mule‑detection and inbound‑payment models that

unmask money mules

at account opening, during ongoing activity and on receipt of funds, delivering 360‑degree protection and faster investigations (Feedzai's inbound payment/mule risk approach is a useful reference: Feedzai inbound payment fraud & mule risk modeling resource).

Close coupling with AML and compliance orchestration - AI that links transaction scoring, KYC signals and alert triage - reduces false positives while meeting reporting obligations and can be evaluated in a phased sandbox; Protecht's guidance on AI for AML and compliance highlights how automation and cross‑team workflows make that balance achievable (Protecht AI for AML and compliance guide).

Together these approaches turn transaction streams into timely, explainable decisions rather than after‑the‑fact forensics, shrinking the window for organised fraud to act.

Infrastructure teams at the Public Works Department and the Central Electricity Board can turn aging assets into data-rich, resilient networks by combining IoT sensors, drone inspections and AI predictive models: IoT nodes and long‑range, low‑power sensors feed condition data - temperature, vibration, humidity and water levels - into real‑time analytics so crews know where to act before a failure becomes a blackout or a washed‑out road (see Semtech's flood‑sensor approach and sensor guidance).

Drones with visible‑light and thermal cameras accelerate inspections of lines, substations and bridges, spotting hotspots or structural wear from the air and enabling safer, faster repairs; docked, autonomous drones even allow on‑demand scans after storms to prioritize crews (see drone inspection and CBM best practices).

Layering machine‑learning predictive maintenance software on top turns those signals into precise failure forecasts, reduces unnecessary scheduled checks, extends equipment life and optimises maintenance budgets - imagine a thermal drone flagging a substation hotspot at dawn and a predictive model routing the nearest crew before commuters notice.

Practical pilots should start with a single feeder or tunnel (the Smart Tunnel case shows sensor+AI in action), prove time‑to‑repair gains, and scale under clear operations and training plans (utility drone inspection and condition-based maintenance best practices, AI predictive maintenance services for utilities).

Approach	What it delivers
Drone inspections (thermal & visible)	Faster, safer asset surveys and anomaly detection (Utility Analytics / Foresight)
IoT sensor networks	Continuous condition monitoring & early flood/water alerts (Semtech / Flood sensor networks)
AI predictive maintenance	Failure forecasting, fewer needless checks, longer asset life (DAC.digital / ES Systems)

Protecting Mauritius' beaches - vital to tourism and threatened by accelerating erosion - calls for more responsive monitoring than sporadic field surveys or costly, low‑resolution satellite passes; a recent study,

Automated Shoreline Detection in Mauritius

shows how AI‑enabled drones and computer‑vision workflows can fill that gap by autonomously extracting shorelines from aerial imagery using techniques like Canny edge detection and contour filtering (AI‑driven cost savings in Mauritius' public sector).

The project built a local aerial dataset (monthly maps of three erosion‑prone sites since November 2022) so models learn Mauritian coastlines instead of relying on foreign data, and early results demonstrate the promise - and limits - of simple edge‑detection: some shorelines are identified reliably, while scenes with obscuring landscape features demand more advanced machine learning.

By cutting dependence on slow manual surveys and turning regular drone flights into actionable time‑series for planners, this approach offers a budget‑aware path to smarter coastal protection that aligns with public‑sector efficiency goals.

Item	Detail
Dataset start	Monthly aerial maps since November 2022
Sites monitored	Three erosion‑prone Mauritian beaches
Techniques	Canny edge detection, contour filtering; drone imagery
Preliminary result	Shorelines detected in many images; some failures where landscape features obscure the coast - needs more advanced ML

The Central Electricity Board (CEB) now sits at the centre of a fast‑moving energy story: a record evening peak of 545.7 MW and an abrupt 42 MW jump in demand over a few weeks have exposed ageing turbines and prompted short‑term fixes such as a proposed “powership” (90–110 MW) while batteries, a GIS upgrade and renewables are pushed forward to protect supply (Mauritius 545.7 MW electricity peak news report, Mauritius powership proposal ministerial briefing on energy crisis).

Demand tends to peak between 6–9 PM - picture neighbourhoods humming with air‑conditioners as the grid strains - and that nightly surge makes short‑term forecasting and demand management essential; longer‑horizon analysis and statistical tools (including Gompertz diffusion and ANN approaches) can sharpen peak predictions so planners know when storage or flexible generation must kick in (Gompertz and ANN peak demand forecasting research for Mauritius energy).

The practical takeaway for the CEB: combine smarter, weather‑aware demand forecasting with targeted storage, efficiency campaigns and staged procurement so outages are avoided without locking in another decade of high‑cost thermal capacity.

Indicator	Value / note
Record peak	545.7 MW at 9 PM (Jan 22 report)
Recent demand spike	+42 MW in a few weeks (ministerial briefing)
Powership capacity	Proposed 90–110 MW temporary solution
Forecasting tools	Non‑homogeneous Gompertz + ANN methods for peak prediction

“We are teetering on the edge of a precarious agreement. This is far from comfortable. A nation cannot allow its energy security to hinge on the whims of a machine or the caprices of the weather. No!” - Minister Patrick Assirvaden

Public safety in Mauritius is being reshaped by data and systems that turn camera feeds, call logs and digital forensics into faster, smarter action: the nationwide Safe City rollout - with roughly 4,000 cameras feeding video analysis, facial recognition (reported >95% accuracy) and licence‑plate reads (>99%) - aims to cut emergency call‑to‑dispatch times to under 15 minutes and lift handling efficiency by about 60%, while also helping resolve cases (120 criminal cases credited to Safe City in 2021) and reducing roadway accidents through intelligent road surveillance (reported ~45% reduction); these operational gains sit alongside a strengthened Police IT Unit that handles CCTV and social‑media evidence, digital forensics and first‑responder support to ensure data is admissible and actionable (see the Safe City deployment overview and the Mauritius Police IT Unit).

Practical emergency planning matters too: modern response plans and mass‑notification playbooks - like those summarized in an emergency response guide - are essential to turn alerts into coordinated multi‑agency action rather than noise, and to make sure technology shortens lives‑at‑risk timelines instead of creating bureaucracy.

The combination of sensors, clear roles, and rehearsed communications gives Mauritius a realistic path to faster rescues and fewer preventable incidents, turning dense camera arrays into an integrated public‑safety nervous system rather than surveillance for its own sake.

Metric	Reported value / note
Cameras with facial recognition	~4,000 (Safe City deployments)
Target call‑to‑dispatch time	Under 15 minutes (Safe City)
Emergency handling efficiency	Improved by ~60% (Safe City)
Roadway accident reduction	~45% via intelligent road surveillance (Safe City)
Criminal cases attributed	120 cases resolved in 2021 (Safe City)

« Les images de ces catégories de personnes seront téléchargées sur la base de données du système par des policiers formés et autorisés », a expliqué le chef du gouvernement.

Education and workforce development are the linchpin for turning Mauritius' strong AI readiness into everyday public‑sector value: targeted upskilling programs and clear skills‑mapping can move civil servants from manual processing to roles in digital process management, AI oversight and citizen experience design, while local language fluency ensures tools serve Mauritian communities, not sideline them.

Start with practical, high‑impact courses and short professional tracks that deliver immediate gains - Treeshake's leadership framework urges starting with “efficiency gains” to build trust before scaling to transformation (Treeshake AI leadership Mauritius report) - and link those programs to the national priorities named in the Mauritius AI Strategy so education pipelines feed agriculture, health, FinTech, transport, manufacturing and the ocean economy (OECD Mauritius Artificial Intelligence Strategy).

Practical skills mapping - certifications for prompt design, model validation, records automation and Creole‑capable NLP - lets MITD and training providers prove quick wins, anchor funding requests, and make the promise of a “Smart Island” tangible in classrooms, council chambers and help desks across the island (Nucamp AI Essentials for Work bootcamp - guide to using AI in Mauritius government), so a student can graduate able to build a Creole tutor bot or audit an OCR pipeline within months, not years.

Indicator	Value / note
Oxford Insights (2024) AI readiness	61st globally; 1st in Africa (Treeshake)
Mauritius AI Strategy priority sectors	Agriculture, Health, FinTech, Transport, Manufacturing, Ocean economy (OECD)

“Leaders need to be actively investing in understanding how AI can make a difference for their company today.”

Policy analytics, decision support and explainable AI (XAI) are the governance glue that can make Mauritius' AI ambitions credible - and the 2018 Mauritius Artificial Intelligence Strategy explicitly foresaw a Mauritius Artificial Intelligence Council (MAIC) to steward that work, yet the ten‑member body was never fully institutionalised, leaving a governance gap that still matters for high‑stakes uses in health, finance and public services; restoring a focused MAIC (or a tightly scoped successor) would let policymakers demand explainability as a design principle, require model registries and documentation, and run policy pilots that surface trade‑offs before systems scale.

Explainability isn't academic: tools and practices from the XAI field translate algorithmic choices into human‑readable stories - so a benefits‑eligibility decision, for example, can show which data points tipped the outcome - building trust for citizens and confidence for regulators (see the OECD's Mauritius AI Strategy for the original recommendation and its governance gaps).

Practical platforms that bake governance into workflows - model registries, audit trails, feature‑importance reports and risk matrices - help teams compare impact and risk across sectors and make decisions reproducible; vendors and toolkits such as Dataiku illustrate how a centralised govern‑and‑audit approach can scale oversight without stifling innovation.

Embedding XAI, aligning it to the OECD principles of accountability, fairness and privacy, and turning MAIC's remit into concrete checklists and pilot milestones would change AI in Mauritius from an aspirational roadmap into an explainable, auditable public service - imagine ministers being able to pull a one‑page, evidence‑backed explanation when a model recommendation affects thousands of citizens overnight, instead of a black box and a press briefing.

Item	Detail (source)
Recommended body	MAIC - 10 members (Mauritius AI Strategy, OECD)
Responsible organisation	Ministry of Information Technology, Communication and Innovation (ITCI) (OECD)
Status	Strategy complete; MAIC never fully institutionalised (OECD / dig.watch)
Key policy focus	Accountability, fairness, privacy, robustness; sectoral priorities: agriculture, health, FinTech, transport, manufacturing (OECD / dig.watch)

To turn strategy into service, Mauritius should move from big-picture vision to three practical, budgeted actions: first, close the governance gap the OECD highlighted by fully institutionalising a Mauritius AI Council or equivalent to run model registries, audit trails and sectoral roadmaps (the Mauritius AI Strategy (OECD) underlines this need); second, start with measurable, low‑risk pilots in the Strategy's priority sectors (agriculture, health, FinTech, transport, manufacturing and the ocean economy) while demanding explainability and lifecycle rules up front; and third, invest in people and affordable access - leadership training and short, practical courses will unlock adoption fast, so complement national programmes with targeted upskilling like the 15‑week Nucamp AI Essentials for Work and the phased leadership approach Treeshake recommends to secure quick wins before transformation.

A bold, budget‑aware move - Dr Makoond's proposal to subsidise LLM access at roughly 0.035% of GDP (~$5.15M/year) - shows how small, visible investments can democratise tools and amplify ROI; the immediate test: fund a few proof‑of‑concepts, measure time‑saved and citizen impact, then scale under tightened governance and clear milestones.

Track	Priority action
Governance	Institutionalise MAIC/METC successor; require registries, audits and explainability (OECD)
Skills & Leadership	Deliver short‑course upskilling and leadership cohorts (Treeshake + Nucamp AI Essentials)
Pilots & Access	Run sector pilots in priority areas and pilot subsidised LLM access to test broad public value

“Leaders need to be actively investing in understanding how AI can make a difference for their company today.”