Top 5 Jobs in Government That Are Most at Risk from AI in Mauritius - And How to Adapt

Mauritius's post‑COVID rebound and push to diversify - with services making up roughly two‑thirds of the economy and the World Bank noting government priorities like boosting human capital and digitalisation - make AI a practical priority for public servants, not a distant tech buzzword (World Bank Mauritius overview).

On an island of about 1.26 million people, small gains in productivity or automation can ripple across tourism, finance and public services; the same government agenda that targets 60% renewable energy by 2030 also highlights the need to ease labour and skills shortages, where AI can augment routine administrative work and free staff for complex tasks.

Upskilling is therefore urgent: the AI Essentials for Work bootcamp teaches practical, non‑technical AI skills, prompt writing, and job‑based AI tools - see the full syllabus and registration options for a workplace‑focused pathway into AI adoption (AI Essentials for Work bootcamp syllabus and registration).

To identify the top five government jobs most at risk from AI in Mauritius, the team blended global and local evidence: a rapid review of AI-in-government use cases (from Emitrr's practical breakdown of where AI excels - high-volume data analysis, chatbots, fraud detection - and where humans must remain in the loop) guided a shortlist of functions that are data‑heavy, repetitive, or rules‑based (AI in Government: data analysis, chatbots, and fraud detection - Emitrr); Mauritius‑specific use cases - like Energy Demand Forecasting and documented public‑sector cost savings - helped flag roles tied to operational systems such as utilities, tax and land records (Energy Demand Forecasting in Mauritius, AI-driven public-sector cost savings in Mauritius); finally, emerging ideas about agentic AI in procurement informed assessments of roles that could shift from task automation to autonomous decision support (Agentic AI for strategic procurement - JAGGAER).

Roles were scored against repeatability, data volume, citizen‑facing exposure and legal/ethical risk, then validated by sector use cases so that the final list reflects both technical feasibility and real operational impact - in short, tasks AI can turn from days of manual audits into minutes were prioritised for adaptation planning.

Customs officers at the Mauritius Revenue Authority are squarely in the sights of automation because their core tasks - document checks, HS‑code classification, risk profiling and repetitive valuation audits - map neatly onto proven AI capabilities like OCR, NLP, anomaly detection and x‑ray image analysis; the new “AI Customs Officer” can scan manifests in seconds, flag high‑risk consignments and suggest tariff codes, which already shortens clearance workflows and revenue recovery efforts (AI Customs Officer manifest scanning and HS‑code classification).

International practice and research stress a shift to technology‑centric, data‑driven risk management - supplier codification, mirror analysis and entity profiling are examples - that lets authorities prioritise inspections while speeding low‑risk trade (technology‑centric customs risk management for supply chain security).

For Mauritian officers the practical adaptation is clear: move from manual clearance to roles that oversee AI models, validate flagged cases, manage data quality and join cross‑agency data initiatives; invest in targeted upskilling and IT modernization so dashboards, not paperwork, become the daily workflow, turning processes that once demanded days of manual audit into minutes.

“With a compound annual growth rate (CAGR) of 28.46%, the worldwide artificial intelligence (AI) market is expected to reach an astounding $1.81 trillion by 2030.”

Tax auditors at the Mauritius Revenue Authority face acute risk because routine, high‑volume work - transaction sampling, exception testing and document review - is exactly where AI, machine learning and robotic process automation already add value: machine learning can sift whole populations of transactions to flag anomalies that manual sampling misses, and tax directors who don't use the same tools as tax authorities risk overlooking discrepancies (International Tax Review - Data‑driven tax audits and digitalisation).

Local research into audit quality in Mauritius underlines why this matters: maintaining audit standards depends on matching methodology to changing data and control environments (SCIRP study: Audit Quality in Mauritius).

Practical adaptation means shifting from paperwork to analytics oversight - upskilling in data analysis, AI‑assisted testing, RPA orchestration and controls around automated systems - and gaining familiarity with blockchain and analytics tools that let auditors inspect entire ledgers instead of samples, reducing long audits to focused inquiries (Deloitte: Impact of blockchain on accounting and audit technologies).

The

“so what?”:

those who learn to question model outputs, manage data quality and translate AI findings into policy will move from redundancy risk to indispensable guardians of tax integrity.

Administrative clerks at the Ministry of Civil Service sit squarely in the RPA bullseye because their day‑to‑day - forms, registrations, payroll batches, data transfers and permit processing - is exactly the high‑volume, rule‑based work robotic process automation excels at; Blue Prism's roundup of common RPA use cases shows how bots can mimic keystrokes, upload documents, extract data with OCR and stitch together legacy systems to clear backlogs (RPA use cases and automation tools - AI Essentials for Work syllabus), and real world examples include bots sorting 12,500 emails a month or running 20,000 forensic checks in a week.

For Mauritius that means the routine handoffs that now clog desks can be redesigned: clerks can move into roles that manage bot orchestration, own data quality and exceptions, run process‑mining to find new automation wins, and become the citizen‑facing experts who translate bot outputs into fair, timely decisions.

The practical “so what?” is vivid - stacks of morning forms no longer sit on a counter for days but feed dashboards that flag only the few tricky cases needing human judgement - so targeted upskilling in low‑code RPA, process governance and analytics is the most defensible career move (local AI-driven cost savings in Mauritius - AI Essentials for Work registration).

“We are immensely proud of our digital transformation journey as it has enabled us to deliver better customer service by building rewarding digital engagement through considerate and effective use of innovation, digitization and customer data.”

Traffic police dispatchers in Mauritius face a clear pivot point as intelligent traffic management systems move from research to real‑world deployment: tools that detect incidents, model flows and re‑time signals can take over routine triage and routing so dispatchers no longer spend shifts relaying the next available detour but instead validate AI alerts, manage exceptions and coordinate on‑the‑ground response; solutions like Yunex Traffic's adaptive control and awareAI show how cameras, sensors and algorithms can predict congestion and create “green waves” to keep traffic moving (Yunex Traffic intelligent traffic management).

The national angle matters - the Mauritius AI Strategy explicitly flags transport as a priority sector - so the practical adaptation is to shift dispatch roles toward model oversight, data governance and cross‑agency coordination while starting with high‑impact, trust‑building pilots.

Leadership guidance on sequencing - beginning with quick wins that save time and build confidence - offers the clearest path for dispatchers to move from routine operators to indispensable incident managers (Treeshake: AI leadership for Mauritius).

“Start with what saves time and builds trust,” says Duarte.

Land Registry Officers in Mauritius are squarely in the digitisation spotlight: the Registrar‑General's eRegistry now captures deeds electronically, offers an online dashboard and quasi‑real‑time registration, and has integrated cadastral data back to 1978 so title searches and payments happen with minimal human handling - turning mountains of paperwork into searchable pixels and aerially‑mapped parcels (Digital land registry in Mauritius - DLA Piper analysis).

That efficiency reduces error and speeds transactions, but it also shifts the job: routine recording and data entry are ripe for automation while the hard human work left - resolving discrepancies, detecting forged or counterfeit titles, and managing cross‑department data flows - becomes higher‑stakes because mistakes can trigger costly court battles.

Systems like LAVIMS (cadastral capture, parcel IDs and digital aerial imagery) have already modernised workflows, yet centralised databases still carry risks that blockchain experiments aim to fix by promising immutable, tamper‑resistant records - an attractive option as Mauritius explores virtual‑asset and distributed‑ledger tools to bolster trust in land data (Blockchain for land registry - InfoSystems, Registrar‑General e‑Service transition case study - NRD Companies).

The practical “so what?”: officers who upskill into data governance, title forensics and system oversight move from redundancy risk to indispensable guardians of property rights.

“We like lists because we don't want to die.”

Practical next steps for Mauritius are straightforward and actionable: run targeted, measurable pilots in priority sectors (transport, public governance, energy) to prove value and build public trust while tracking time‑saved and error reduction; lock in sustained governance and funding by activating the MITCI AI Unit, aligning the Mauritius AI Strategy's high‑level vision with Budget 2025–2026 commitments (Rs25M for a Public Sector AI Programme and new incentives for AI start‑ups) so recommendations move from paper to projects; invest in workforce reskilling so clerks, auditors and officers become model‑overseers and data stewards rather than displaced workers - practical training like the AI Essentials for Work bootcamp gives prompt‑writing, tool use and job‑focused AI skills; and strengthen data governance, cross‑agency sharing and ethical safeguards so digitisation improves service delivery without creating new risks.

These steps - small pilots, clear accountability, funded upskilling and tougher data practices - turn strategy into impact and make the familiar promise real: processes that once took days can be reduced to minutes, freeing public servants for higher‑value, trust‑preserving work (Mauritius Artificial Intelligence Strategy - OECD policy dashboard, Mauritius Budget 2025–2026 AI measures - EDB Mauritius, AI Essentials for Work bootcamp syllabus - Nucamp).