How AI Is Helping Government Companies in Brunei Darussalam Cut Costs and Improve Efficiency

Brunei Darussalam is turning early AI investments into everyday savings for public agencies by using AI to personalise services, automate routine administration and sharpen decision-making; Universiti Brunei Darussalam's Applied AI programme - which celebrated its first cohort of six graduates - explicitly trains students on projects like intelligent HR systems and digital public‑health tools that support workforce automation and public‑service efficiency (Universiti Brunei Darussalam AI initiatives).

National moves such as the voluntary Brunei Darussalam voluntary AI guidelines and the Personal Data Protection Order (PDPO 2025) are tightening trust around data while enabling cost‑saving pilots, and targeted upskilling - for example, Nucamp's Nucamp AI Essentials for Work bootcamp - helps government teams learn practical prompt‑writing and automation skills without needing a technical degree, so savings actually land in departmental budgets.

“This first cohort of Applied AI graduates marks an important milestone, not only for SDS but for Brunei Darussalam's journey towards a digitally empowered future. These students represent the next generation of innovators, equipped with the technical knowledge, critical thinking skills, and adaptability to drive transformation in industry and society. Their success reflects our vision to produce graduates who are not just consumers of technology, but also its creators and leaders.” - Senior Professor Dr Chandratilak De Silva Liyanage

Brunei's AI strategy is deliberate and practical: government agencies are rolling out toolkits, pilots and guidelines that aim to shave time and cost from core public services while building capacity for the long term.

The Prime Minister's Office, working with the Public Service Commission and partners such as the Commonwealth Secretariat and Intel, has introduced a smart applicant screening system and is piloting StrategusAI to speed hiring and sharpen policymaking - a timely move inside a $552.34 million PMO budget where 58% goes to staff costs (PMO's smart recruitment and StrategusAI initiative).

Parallel steps from MTIC include drafting responsible‑use AI guidelines and funding over $25 million of e‑government projects - from a GOV.BN 2.0 overhaul to a $5M records management system and an $8.74M DriveBN push that trims workflow from 104 processes to just 33 - tangible changes that reduce paperwork and speed service delivery.

Capacity and ethics are baked in through regional programs too: the AI Ready ASEAN Programme at UTB targets 20,000 Bruneians with practical, values‑based AI skills so savings land where they matter most - in faster, fairer public services.

“As ASEAN moves forward in its digitalisation agenda, we must ensure that our citizens - especially our youth, educators, and parents - are not merely passive consumers of technology, but active participants in shaping it. This means building digital confidence, nurturing ethical awareness, and developing the right set of skills and mindsets to responsibly navigate the AI-powered world,” - Yang Mulia Dr Haji Azman bin Ahmad, Permanent Secretary (Higher Education), Ministry of Education

When government companies in Brunei evaluate platforms and procurement for enterprise AI, practical concerns win the day: choose deployable, secure LLMs with clear pricing and management, not just flashy demos.

Platforms like BytePlus ModelArk PaaS enterprise LLM deployment platform package private‑ or public‑cloud options, token‑based billing and a dashboard for model management - a real advantage when ministries need audit trails and predictable costs - and some ModelArk pages even highlight promotional token bundles (500k free tokens) that make pilots cheaper to start.

Procurement teams should match platform features to sector needs already proven in Brunei - from AI‑driven personalization in retail and marketing to predictive maintenance in manufacturing - and factor in common barriers flagged across local reports, such as a limited talent pool and high implementation costs (estimated average AI project costs range from BND 100,000 to BND 500,000).

For public buyers the smartest route is staged procurement: run a tight pilot tied to a measurable service outcome, use tokenized or usage‑based contracts to limit upfront spend, and pick platforms with enterprise security and model‑management tools so savings on staff time and errors translate into real budget wins.

Learn more about local sector use cases in AI in Brunei: marketing industry use cases.

Darussalam Assets offers a clear, local example of how enterprise AI cuts government hiring costs: by embedding SAP Business AI into its SAP SuccessFactors suite the group - spanning 30 subsidiaries and more than 9,000 employees across 14 sectors in Bandar Seri Begawan - automated job‑description generation, CV parsing and on‑the‑fly, competency‑based interview questions (even integrated into Microsoft Teams), trimming recruitment from several months to roughly four weeks and delivering a reported 4x efficiency gain and ~75% reduction in time‑to‑hire; the result is faster, fairer hiring across everything from neurosurgeons to call‑centre agents, and a unified talent view that turns scattered spreadsheets into instant group‑wide insight (see the SAP case study and Computer Weekly profile for details).

That speedup - shrinking multi‑month waits into a single month - is the kind of operational shock that actually frees budget for training and service improvements rather than just shifting cost around.

“As a large and diversified organisation, our adoption of SAP SuccessFactors solutions transformed HR's impact on the business. In terms of talent management and recruiting, we can now look at talent-pool data and run reports from across our group of companies in a matter of seconds.” - Salehin Basir, Senior Human Capital Development Manager, Darussalam Assets Sdn Bhd

Brunei's education and research ecosystem is deliberately building the talent pipeline that government companies need to turn AI pilots into budgetary wins: Universiti Brunei Darussalam's undergraduate Bachelor of Digital Science now offers majors in Data Science, Artificial Intelligence & Robotics and a three‑year Applied Artificial Intelligence stream, and its programmes blend lab work, industry certifications (IBM, Google) and real projects so graduates arrive with practical skills, not just theory (Universiti Brunei Darussalam Bachelor of Digital Science program).

That hands‑on focus paid off visibly when students used large language models with a NAO robot in the Intelligent Systems Lab and presented demonstrations to the Sultan, illustrating how classroom work can prototype public‑service tools; UBD's broader AI and innovation programmes explicitly tie coursework and research to the Digital Economy Masterplan 2025 and new workforce needs, including the Master of Digital Public Health and industry partnerships that funnel applied research into government use cases (UBD AI and innovation programmes driving Brunei digital transformation).

Short courses, the Institute of Applied Data Analytics and career services help translate graduates into deployed teams, creating the concrete skills that trim implementation time and protect procurement budgets - picture a candidate who can ship a usable prompt‑engineering workflow on day one rather than months into a project.

AI is already reshaping healthcare, education, government services and industry in Brunei by turning data into faster decisions and fewer busy‑work hours: hospital pilots and toolkits are using AI-driven imaging and predictive analytics to speed diagnosis and forecast admissions, while administrative automation trims the late‑night “pajama time” clinicians spend on notes and coding so staff can focus on patients and schools can redeploy time to teaching and research; for a practical roundup of local tools see the BytePlus guide to best AI tools for healthcare in Brunei, and for a clear case for a measured, safety‑first rollout read the InterSystems report on generative AI in healthcare.

Simple use cases - automated triage chatbots, EHR summarisation, and healthcare resource forecasting to optimise bed allocation and staffing - deliver real cost and time savings (some vendor solutions even report up to 2.5 hours saved per provider per day), a vivid reminder that modest automation can free budgets for training and better services rather than just shifting costs around; local government planners should prioritise staged pilots that link measurable service outcomes to procurement and workforce upskilling (see practical forecasting use cases in Nucamp AI Essentials for Work syllabus on healthcare resource forecasting).

“I've been practicing now for over 25 years. And when I see a patient, I can't tell you how important it is for me to be able to see body language. When I'm using NextGen® Mobile, I'm able to concentrate on those nonverbal cues that otherwise I would completely miss if I were typing away madly into the computer.” - Brian Heimer, MD, Medical Director, Digital and Virtual Health, American Health Network, Optum

Barriers to safe, cost‑saving AI in Brunei are practical and policy‑focused: the country still favours a principles‑first, flexible approach rather than binding rules, leaving implementation risks in areas like data governance, procurement costs and workforce readiness that can slow pilots and inflate budgets.

Brunei's voluntary AI guidelines set out seven guiding principles - including transparency & explainability, security & safety, fairness & equity, and data protection & governance - which steer agencies toward accountable deployments (Brunei voluntary AI guidelines); national messaging from MTIC and AITI stresses building “safe, ethical and inclusive” systems as the Digital Economy Masterplan evolves (MTIC guidance on digital foundations for the Digital Economy Masterplan).

Regional frameworks - notably the ASEAN Responsible AI Roadmap and Expanded Guide - give Brunei pragmatic, interoperable tools to audit risk, require human oversight and encourage staged pilots so ethics and savings travel together rather than collide at scale (ASEAN Responsible AI Roadmap and Expanded Guide).

The upshot: with clear principles, staged procurement and stronger data rules, Brunei can turn ethical guardrails into predictable cost savings that actually free up budgets for training and services - a high‑leverage win for a “small, highly connected” state.

“However, with these opportunities come new policy responsibilities. For small, highly connected nations like ours, it is critical that we get the foundations right - building systems that are safe, ethical, and inclusive.” - Hj Hairul Mohd Daud, MTIC (reported in The Scoop)

Start small, stay measurable and build the plumbing first: pick one high‑value, tightly scoped service (for example a triage chatbot or applicant screening) and run a staged pilot with clear KPIs so savings are proven before scaling; use platforms that offer predictable, token‑based billing to cap upfront spend (see BytePlus ModelArk for token pricing and enterprise model management) and avoid “big bang” procurements.

Pair any pilot with a deliberate data foundation - move from siloed sources to a logical, semantic layer and built‑in governance so models get context, reduce latency and accelerate time‑to‑insight rather than multiplying errors (Denodo's approach to agentic AI highlights this).

Treat people as the linchpin: embed role‑based upskilling and data‑literacy programs so business teams can spot high‑impact use cases while technical teams handle guardrails and RAG design (Forrester's public‑sector playbook recommends targeted, measurable training tied to real agency data).

Finally, measure what matters - pretrain baselines, run capstone projects on live data, track tool adoption and iterate - so each pilot turns ethical, governed AI into predictable budget wins that free resources for service improvements.

“The robust and flexible architecture supports seamless scalability, so that AI applications can evolve alongside the changing demands of public service.” - Felix Liao, Denodo

Measuring ROI for government companies in Brunei starts with a clear baseline and disciplined cost visibility: capture direct costs (salaries, cloud compute, data preparation) and indirect costs (infrastructure, compliance, energy) and then tie savings to service KPIs so pilots translate into real budget line items rather than vague productivity claims; Apptio's playbook for AI cost management recommends TBM/FinOps practices to pull all technology spend into a single view and prove time‑to‑value (Apptio - The complex costs of AI investments, funding and ROI tracking).

Use a simple ROI formula (Net benefits ÷ Total costs) to capture automation gains such as reduced time‑to‑hire or fewer clinician admin hours and report both quantitative and qualitative impact - as advised in RSM's cost‑optimization guide (RSM guide to maximizing efficiency and ROI in AI initiatives).

For Brunei specifically, anchor pilots to the Digital Economy Masterplan and pick staged, token‑priced platforms so procurement risk stays low; local context and use cases are covered in BytePlus's overview of AI in Brunei, which highlights sector priorities and practical deployment models (BytePlus analysis: How AI is transforming Brunei Darussalam).

Finally, track funding sources and tempo - Apptio notes many organisations fund AI via internal allocation, realised savings, or dedicated funds - so each pilot documents where savings will be reallocated and when.

“AI agents can revolutionize the way we work and unlock possibilities that were once unimaginable.” - Dan Diasio, EY Global Consulting AI Leader

Future progress looks achievable if Brunei turns its careful, phased digital strategy into disciplined operational steps: the imminent six‑month BruneiID trial and the BND146.5 million allocated to digitisation create a practical runway to scale AI pilots in healthcare, taxation and banking while testing adoption, privacy and technical feasibility (BruneiID trial and digital roadmap).

Recommendations: anchor every pilot to the Digital Economy Masterplan and Wawasan Brunei 2035 so projects feed national diversification goals; prioritise measurable KPIs and ROI tracking from day one; lean on regional interoperability and ASEAN collaboration to avoid reinvention and unlock cross‑border services as the region pursues a $2 trillion digital economy; and invest in rapid, practical upskilling so civil servants can deploy prompt‑driven automations and governed workflows immediately - short, applied courses like the Nucamp AI Essentials for Work bootcamp accelerate usable skills for non‑technical teams.

Finally, treat policy implementation and workforce development as twin priorities - Brunei's transition is structural, so pairing legal and technical foundations with focused training and measurable pilots will turn early investments into real cost savings and sustained public‑service improvements (see broader context and policy analysis in the Springer volume on Brunei's economic transition).