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

AI is no longer abstract theory for Brunei Darussalam's public sector - it's a suite of tools that can read documents, extract data, automate repetitive tasks and even generate answers, so roles that process permits, audit forms or routine applications are now exposed to disruption while agencies gain faster, cheaper services; IBM's clear primer on what AI does explains how generative and narrow AI automate work, and a local example shows AI-driven recruitment with SAP SuccessFactors helped Darussalam Assets hire four times faster and cut time-to-hire by 75% (Darussalam Assets AI recruitment case study).

To govern that change, the April 30, 2025 Voluntary AI Guidelines for Brunei offer practical expectations for transparency and incident reporting (Brunei Voluntary AI Guidelines (April 30, 2025) overview), and upskilling is essential - consider practical options like Nucamp AI Essentials for Work 15-week bootcamp to learn promptcraft, tool workflows and on-the-job AI safety so civil servants can shape how automation helps citizens.

“The model is just predicting the next word. It doesn't understand,” explains Rayid Ghani, professor of machine learning at Carnegie Mellon University's Heinz College of Information Systems and Public Policy.

Roles were evaluated against three practical, Brunei-focused criteria drawn from public-sector AI literature: susceptibility to routine, high-volume processing (document-heavy tasks that document automation can shrink from weeks to seconds), the need for cross‑agency data integration and discretionary human judgement with attendant privacy risks, and the realistic pace of adoption given legacy systems and budget limits.

Sources informed each axis - privacy, equity and employment tradeoffs from CentralSquare's public-sector review, opportunities and barriers to automation from Trinus' trends analysis, and concrete document-automation use cases from Experlogix - were used to map which government jobs have the most task-level overlap with current AI capabilities.

Scores combined task-exposure, data-sensitivity and implementability to produce a top‑5 list tailored to Brunei's administrative structure, so the final recommendations point to reskilling paths where automation yields faster citizen outcomes rather than abrupt job loss; think of approvals that once took weeks now closing in seconds, freeing staff for judgement calls that AI can't make.

Data Entry / Administrative Officers in Brunei sit at the frontline of paperwork: they prepare and sort documents, verify source records, enter text and numbers into government databases, run backups and respond to information requests - tasks laid out in the official BIICF Data Entry Job Profile Brunei.

As Ministries move systems onto shared platforms like SSM, where HR Administrators now update job data and access is restricted to government networks, the day-to-day choreography of manual entry and secure handling is changing fast (SSM HR System Guide - Jabatan Perkhidmatan Awam Brunei).

That combination - high-volume, structured inputs plus centralised systems - means OCR, automated validation and low-code tools (Excel, Power BI, Power Apps) can absorb predictable work, leaving human officers to resolve exceptions and protect privacy: imagine scanning a stack of forms and only keeping the few with mismatched IDs for human review.

Practical adaptation is clear from the BIICF skills map - boosting database, data-governance and basic analytics skills turns a vulnerable clerk into the team member who designs the safe, auditable automation that keeps services running.

Customs and immigration officers in Brunei are squarely in the path of practical AI tools that speed checks and sharpen risk detection: from automated document verification and biometric ID matching to computer‑vision X‑ray analysis that flags anomalies and suggests commodity codes for review.

Systems like CBP's inventory show how AI can triage vessels, cargo and travelers with machine‑assisted detection, entity‑resolution risk models and anomaly bounding boxes on images - so mundane checks that once clogged arrival halls can be reduced to targeted reviews, not wholesale desk cuts (DHS CBP AI Use Case Inventory).

At the same time, immigration research stresses that facial biometrics, predictive analytics and translation tools speed processing but raise privacy and bias questions that require clear rules and human oversight (AI and automation in immigration - EndeVio insights), while customs vendors highlight intelligent document processing for manifests and declarations to cut manual data entry and false positives (Role of AI in customs and border security - iCustoms blog).

For Brunei officers the practical win is obvious: when an X‑ray image lights up with a red bounding box around a hidden pocket, staff can focus scarce expertise where it matters and keep borders both efficient and accountable.

“CBP has successfully implemented facial biometrics into the entry processes at all international airports, known as Simplified Arrival, and into the exit processes at 57 airport locations. CBP also expanded facial biometrics at 39 seaports and all pedestrian lanes at both Southwest Border and the Northern Border ports of entry. To date, CBP has processed more than 540 million travellers using biometric facial comparison technology and prevented more than 2,000 impostors from entry to the U.S.” - U.S. Customs and Border Protection

Corporate Tax Auditors - often titled Tax Compliance Officers - carry the technical responsibility in Brunei of examining accounting books, business records and financial affairs to verify that companies declared the correct income and paid the right tax; PwC's Brunei corporate tax administration guidance explains that companies must file an Estimated Chargeable Income within three months, a complete income tax return (with general ledger and MOFE‑specified tax schedules since 2022) by 30 June, and can face desk or field audits when figures don't align (PwC Brunei corporate tax administration).

The stakes are vivid: audits can reach back up to six years (longer for suspected evasion), late or missing returns risk a BND 10,000 fine and, in default, imprisonment - a single mismatched ledger can therefore trigger a lengthy review.

Practical adaptation in this role means blending judgement with analytics: anomaly detection, SAF‑T‑style structured exports endorsed in international audit practice and statistically sound sampling let auditors triage high‑risk cases so human reviewers focus on interpretation, transfer‑pricing and classification questions that AI can't resolve; technical upskilling in data analysis, audit sampling and evidence‑based documentation turns vulnerability into an opportunity to manage risk more efficiently (audit guidance for tax professionals).

Permit & Licensing Officers who manage land and building permits in Brunei operate inside a tightly centralised system - land use, planning and title rules rest with the Sultan and his cabinet, and transfers often require approval by “His Majesty in Council,” a process the U.S. investment report calls lengthy and sometimes opaque - so the routine, high-volume checks that make up most permit workloads are prime targets for automation, but the high‑stakes decisions about tenure, eminent domain and Temporary Occupation Licences (TOLs) still demand human judgment (Brunei - Context and Land Governance; 2023 Investment Climate Statements: Brunei).

In practice, OCR, plan‑comparison tools, GIS overlays and rules‑based validations can collapse days of clerical checking into minutes for routine applications, leaving officers to resolve exceptions, complex zoning questions and politically sensitive approvals - picture a single permit flagged for review because a proposed subdivision touches state reserve land and now must progress to a council that can slow the file for weeks.

To stay indispensable, licensing staff should pair technical skills (GIS, document automation and digital case‑management) with deeper legal literacy on the Land Code, Town & Country Planning Act and tenure classes, while following the transparency and incident‑reporting expectations found in national AI guidance for public agencies (Voluntary AI Guidelines overview).

Traffic & Transportation Enforcement Officers in Brunei face a near-term shift as camera-enabled, AI-driven systems move enforcement from occasional patrols to continuous, targeted detection: solar-powered, pylon-style smart cameras and in-vehicle systems can spot rolling stops, illegal turns, mobile‑phone use and seatbelt violations, match plates with registries and triage incidents for human review - turning many routine roadside checks into algorithmic triage that reserves officers for complex, on-scene judgments.

Evidence from recent studies shows these systems do more than catch offenders; they change behaviour and cut accidents, so deploying them thoughtfully could shrink crash rates at busy intersections while freeing officers for investigations (LSE analysis: How AI traffic enforcement makes roads safer).

Practical device options - edge detectors that retrofit existing cameras and report dozens of violation types in real time - make pilot projects technically straightforward (Adaptive Recognition Enforce BOX traffic violation detector product page).

At the same time, privacy, data‑retention rules and community trust matter: examples from other jurisdictions show safeguards, independent impact assessments and narrow retention windows are essential if Brunei is to reap safety gains without eroding public confidence (Discussion of automated enforcement camera privacy concerns).

The vivid takeaway: a bright pylon that flashes a warning and then sends only the violation clip for human review can turn an intersection from a hazard into a deterrent, but only if rules and oversight move as fast as the cameras themselves.

“Our findings offer rigorous evidence that AI-powered cameras on the road improve traffic safety by addressing key enforcement challenges,” Dr Aaron Cheng said. “The impact is striking.”

For Brunei's civil service the practical roadmap is clear and manageable: follow Deloitte's five force‑multipliers - tell a compelling story about why change matters, build visible transformation leadership, galvanise a committed 25% of teams for pilots, redesign work and reskill staff, and harness GenAI with strong data governance (Deloitte: Setting change in motion).

Start small and public - pilot OCR + exception‑review workflows in data entry, cargo X‑ray triage for customs, and automated sampling for tax audits so a stack of forms can be scanned and only the mismatches land on a human's desk, not weeks in a backlog; pair each pilot with the Voluntary AI Guidelines and clear transparency, incident‑reporting and oversight so citizens and officers keep trust (Brunei Voluntary AI Guidelines overview).

Policymakers should fund focused reskilling paths - practical courses that teach promptcraft, safe tool workflows and on‑the‑job use cases - while HR leaders redesign roles to elevate judgement tasks; one affordable, employer‑friendly option is the 15‑week Nucamp AI Essentials for Work programme to fast‑track staff from repetitive tasks into audit, oversight and exception handling roles (AI Essentials for Work syllabus).