The Complete Guide to Using AI in the Government Industry in Viet Nam in 2025

Vietnam's government in 2025 sits at a tipping point: official strategy, new laws and multi‑billion funds are turning the country from AI adopter into regional innovator, and public services are a priority playground for that shift.

Rankings such as World AI Index (Vietnam 6th in 2025, AI trust 3rd) and a growing market (USD 753.4M in 2024) show why ministries must move fast on data governance, cloud and model sovereignty; see the detailed State of AI in Vietnam for 2025 for the full picture.

Concrete building blocks - 5G rollouts, dozens of data centers and national datasets - mean pilots in healthcare, logistics and e‑gov can scale, but talent gaps and fragmented data remain critical hurdles highlighted in analyses of Vietnam's regulatory landscape.

Practical, workplace‑focused training helps civil teams turn policy into projects; explore the Nucamp AI Essentials for Work syllabus to build prompt and AI tool skills that produce measurable public‑service wins.

Bootcamp	Details
AI Essentials for Work	15 Weeks - Learn AI tools, prompt writing, and practical AI skills for any workplace; early bird $3,582, regular $3,942; paid in 18 monthly payments. Nucamp AI Essentials for Work syllabus

“We shouldn't just be end-users of foreign technologies, we should create our own, by Vietnamese, for Vietnamese,” he said.

What will happen in 2025 is less about promises and more about rules, money and concrete levers the state can pull: the National Assembly's June 14, 2025 adoption of the Law on Digital Technology Industry (DTI Law) creates Vietnam's first high‑level AI framework - introducing a risk‑based classification for AI systems, mandatory labeling of AI‑generated products, regulatory sandboxes and explicit prohibitions on harmful uses - so ministries can now move from pilots to regulated scale (see the DTI Law overview).

Expect targeted fiscal and land incentives, fast‑track visas and tax breaks to attract top AI talent and projects, while the government's National Data Development Fund (NDDF) - with an initial capitalization reported at USD 38.4 billion - will bankroll datasets, sovereign compute and model development to support “Make in Vietnam” AI. The law passed with overwhelming political backing (441 of 445 deputies voted in favour), and while many obligations kick in with the law's full effect from 1 January 2026, several investment incentives and sandbox arrangements start earlier, giving agencies a narrow window to align procurement, data governance and cloud strategy with new compliance rules; for practical next steps see summaries of the DTI Law and the 2025 AI sector outlook.

Item	Key detail
DTI Law adopted	14 June 2025
Full effect	1 January 2026 (selected incentives earlier)
NDDF initial capital	USD 38.4 billion

AI is already delivering tangible wins in Viet Nam's public services, but it's more patchwork than panacea: targeted projects - from VinBigData's 1K Vietnamese Genome Project (which uncovered more than 40 million genetic variants) to Viettel's dialect-aware speech synthesis and Zalo's Kiki assistant - show how locally tuned models solve real problems, while citizen uptake of e‑government is driven by perceived service value, empowerment and even fear of COVID-19 (a 2022 study points to those three drivers).

At the same time, adoption remains nascent and ethics, oversight and skills are still catching up, so ministries should treat AI as an efficiency multiplier rather than a turnkey fix.

Recent rollouts of digital ID and biometric checks prove the point: nearly 40% of administrative procedures are now completed fully online and biometric verification has already rendered roughly 86 million bank accounts inoperable as fraud controls tighten - a sharp, memorable example of scale and consequence.

For policymakers and program managers, the lesson is practical: pair citizen‑centred e‑service design (see the Business Perspectives study) with national strategy actions and sector reporting (background and program examples collected by Asia Society) to move from pilots to trustworthy, measurable public‑service impact.

Metric	Source / Figure
Administrative procedures completed fully online	Nearly 40% (BiometricUpdate)
Government entities using data-based management	73 of 84 entities (BiometricUpdate)
Rural mobile broadband reach	99.3% of villages and hamlets (BiometricUpdate)
Genetic variants in 1K Vietnamese Genome Project	More than 40 million variants (Asia Society)

Vietnam's AI governance is moving from strategy to rulebook: the long‑standing National Strategy on AI (2021) sets an ambition to become a regional innovation hub, while recent policy work drills into practical levers - data protection, standards, incentives and experiment zones - to make that ambition operational.

Key building blocks include national standards (TCVN 13902:2023 and TCVN 13903:2023), sectoral measures like Decree No.13/2023 aligning privacy rules with global norms, and the draft Digital Technology Industry (DTI) and Personal Data Protection (PDP) laws that together introduce concrete obligations - mandatory labeling of AI‑generated products, prohibited high‑risk uses, two‑year regulatory sandboxes for controlled trials, and investment incentives such as 150% R&D expense deductions and multi‑year tax breaks for specialist talent.

Policymakers are also debating governance design: a central coordinating body, transparent oversight and public consultation channels are proposed to balance innovation with accountability, and controlled tools (sandbox, tax relief, R&D funds) are explicitly recommended to spur “Make in Vietnam” AI without sidelining ethics.

The practical takeaway for agencies is clear: blend compliance steps (data consent, transparency) with procurement and pilot rules so pilots become scalable public services rather than one‑off experiments - think mandatory AI labeling and sandboxes as the safety rails for rapid, responsibly governed deployment (details in Vietnam Briefing's regulatory overview and Nhandan's policy synthesis).

Instrument	Key point
National Strategy on AI (2021)	Sets long‑term goal to build Vietnam as an AI innovation hub and legal framework foundation
Decree No.13/2023/ND‑CP	Aligns data protection with international standards (privacy/data rights)
Draft DTI Law	Labels AI content, bans harmful uses, offers sandboxes and investment/tax incentives
Draft PDP Law	Creates comprehensive personal data protections; enables R&D with informed consent and withdrawal rights
TCVN AI Standards (2023)	National terminology and trustworthiness frameworks for AI systems

“Since AI is not a legal entity and does not possess will, morality, or legal responsibility, any negative consequences arising from its use must be explicitly attributed to the individuals or organisations involved.”

Vietnamese-language NLP is now a practical foundation for government AI: open datasets and strong benchmarks mean translation, intent detection and named‑entity tools can be built without starting from scratch.

The nlpprogress repository documents a mature stack - word segmentation models like UITws‑v1 hit F1 ≈ 98.06, POS tagging tops out around 96.8 accuracy with PhoBERT‑large, and NER systems using PhoBERT variants regularly score in the mid‑90s - while the PhoMT corpus (3.02M sentence pairs) supports high‑quality EN↔VI translation (mBART EN→VI BLEU 43.46).

There are also domain datasets for slots/intent (PhoATIS), COVID‑era NER (PhoNER_COVID19) and Text‑to‑SQL (ViText2SQL) that speed deployment of chatbots, e‑forms and automated reporting - although practitioners should watch dataset quirks (VLSP's syllable‑split names can distort pipeline evaluations).

For agencies planning Vietnamese LLMs or localized chat assistants, the combination of large parallel corpora, strong transformer backbones (PhoBERT, vELECTRA, PhoNLP) and national model ambitions means building reliable, locally tuned language services is feasible within a government program; see the detailed Vietnamese NLP benchmarks for implementation priorities and model choices.

Task / Dataset	Top model & metric
Word segmentation (VLSP/VLSP2013)	UITws‑v1 - F1 98.06
POS tagging (VLSP 2013)	PhoBERT‑large - Accuracy 96.8
Named Entity Recognition (VLSP / PhoNER_COVID19)	PhoBERT‑large - F1 ≈ 94.5–94.7
Machine Translation (PhoMT)	mBART - EN→VI BLEU 43.46 (PhoMT, 3.02M pairs)
Text‑to‑SQL (ViText2SQL)	IRNet - Exact Match 53.2

Begin pragmatically: pick one high‑value service (e.g., e‑forms, health triage or logistics routing) and map the data, risks and measurable KPIs you need before code - this “map first” step turns ambition into a pilot that regulators and auditors can approve.

Next, align procurement and design with the new DTI Law and national standards - use the DTI Law's risk‑based classification, mandatory AI identification rules and two‑year regulatory sandbox as the safety rails for controlled experiments (see the DTI Law overview at DTI Law overview on Vietnam Briefing) and follow TCVN trustworthiness standards so explainability and labeling are baked into contracts.

Secure funding and incentives early: tap the National Data Development Fund and DTI incentives for R&D and talent, and set a cloud‑first deployment plan so agencies meet the government's cloud targets and leverage local data centers and 5G for scale (details in the State of AI in Vietnam 2025 report).

Pair technical work with a people plan - fast upskilling, university partnerships and targeted recruitment of elite experts - then move from sandbox to procurement by proving outcomes (cost saved, time reduced, citizen satisfaction) and documenting compliance for PDP and sector rules; a vivid test: run a two‑year sandbox that starts as a single-city pilot but is engineered to go national if it meets three operational KPIs, turning a local experiment into a trustworthy, scalable public service.

Step	Quick action / evidence
1. Prioritise & map	Choose one service, define data & KPIs
2. Legal & standards alignment	DTI Law risk classes, TCVN standards, labeling (see the DTI Law overview on Vietnam Briefing)
3. Funding & infrastructure	Use NDDF/DTI incentives, cloud + local data centers (see the State of AI in Vietnam 2025 report)
4. Pilot → Sandbox → Scale	Run up to 2‑year sandbox, measure KPIs, then procure for national roll‑out

Reliable AI in government depends on four concrete building blocks: clean, local data; compliant cloud and data‑centre capacity; GPU‑grade compute; and people who can operate both policy and pipelines.

Vietnam already has momentum - local public cloud offerings are rising as compliance pressures (PDP/data‑localisation rules) push workloads onshore, and the Vietnam Public Cloud market was estimated at about USD 0.96B while broader cloud estimates put the 2024 cloud market in the multi‑billion range - so ministries must plan for hybrid, sovereign deployments rather than foreign‑only clouds (Vietnam public cloud market report - Ken Research (2024)).

Data‑centre capacity is expanding but still immature: experts note current capacity under 40 MW even as telecom and private investors announce major builds, and national players (notably Viettel and FPT) are pursuing AI‑focused infrastructure - Viettel's roadmap cites hundreds of MW and thousands of GPUs for research and services (Vietnam ICT market and foreign investment opportunities - Vietnam Briefing).

Talent is the bottleneck: certified cloud engineers are scarce and university curricula lag, so successful programs pair short, practice‑led training with vendor‑agnostic cloud architectures and targeted hiring to turn pilots into scalable, compliant services.

Item	Figure / Source
Vietnam public cloud market (2024)	USD 0.96 billion (Ken Research)
Vietnam cloud computing market (2024)	~USD 3.58 billion (IMARC / market reports)
Current national data‑centre capacity	Estimated <40 MW (JLL analysis)
Planned AI/data investments	Viettel/FPT announcements: multi‑hundred MW capacity, thousands of GPUs (Vietnam Briefing)
Talent gap	Low certified cloud engineers per 10,000 IT workers; university training limited (market reports)

Vietnam's ethics and compliance checklist for government AI should be practical, visible and enforceable: follow MOST's ethical AI guidelines to make systems human‑centred and safety‑first (see MOST's guidelines on VietnamNet), embed privacy‑by‑design and consent rules that protect spatial privacy and communications, and require explainability and traceability so decisions that affect citizens can be audited.

Make pre‑deployment testing mandatory - run models in secure sandboxes with robust monitoring and rapid response plans - and lock in human oversight and clear accountability lines so any harm is attributed to organisations or individuals, not to

“the AI”

alone.

Require bias audits and diverse training data, explicit user support and redress channels, and continuous updates to standards through cross‑sector cooperation; these are the nine‑point principles reflected in Decision No.1290 (AI management principles) and related guidance that stress transparency, controllability, safety, security and respect for human rights.

A vivid rule of thumb for ministries: no model touches live citizen records until it passes a sandbox test and a governance sign‑off - this single practice turns abstract ethics into an operational fail‑safe that prevents small errors from scaling into national problems.

Link accountability to procurement and KPIs so ethics becomes a measurable part of every contract and rollout.

Checklist item	Action for government teams
Human‑centred design & safety	Adopt MOST principles; require impact assessments and user support (VietnamNet)
Privacy & data protection	Enforce consent, spatial privacy and confidentiality controls; store/process per law
Transparency & explainability	Document inputs/outputs and provide audit trails for decisions (Decision No.1290)
Pre‑deployment testing	Use secure sandboxes and staged rollouts with monitoring/response plans
Accountability & governance	Assign organisational responsibility and redress mechanisms; tie to procurement
Bias & fairness	Mandate algorithmic audits and diverse datasets to prevent discrimination
Collaboration & standards	Engage stakeholders, update guidelines regularly and align with international norms

Vietnam's clearest AI wins so far sit in healthcare: neural networks are speeding diagnoses and extending care into underserved areas, with hospitals using CNNs and deep models for imaging and triage while telemedicine platforms scale access.

Practitioners can find practical tool guidance in BytePlus's guide to neural networks - which highlights TensorFlow, PyTorch and Keras for medical imaging and predictive models - alongside real hospital examples such as a Ho Chi Minh City oncology deployment that cut diagnostic time by about 30% and Da Nang's AI-assisted triage that sped service delivery; meanwhile an implemented telemedicine system reported a 156% increase in patient access, 89% consultation efficiency gain, a 73% reduction in wait time and a 4.8/5 satisfaction score in published case studies.

For government teams planning pilots or state‑owned enterprise rollouts, Nucamp's practical AI roadmap helps translate these healthcare playbooks into measurable savings and KPIs that can be adapted across sectors like agriculture and manufacturing where similar efficiency and access objectives apply; review the detailed case examples and tools to design pilots that prove value before scale.

Example	Key result	Source
Ho Chi Minh City Oncology Hospital (AI imaging)	Diagnostic time reduced ~30%	BytePlus neural network tools for medical imaging and predictive models
Regional telemedicine platform	+156% patient access; +89% efficiency; -73% wait time; 4.8/5 satisfaction	Cientheon Vietnam telemedicine case studies and results

The path to scaling responsible AI across Việt Nam's government is practical and urgent: stitch strong coordination into policy, finance and skills so national ambitions turn into measurable services, not scattered pilots.

Use the new regulatory levers - risk‑based rules, mandatory labeling and two‑year sandboxes - to test and certify systems before they touch citizen records (see the DTI Law and sandbox guidance on Vietnam Briefing), and tap the National Data Development Fund (NDDF) to finance sovereign datasets and compute (NDDF capitalization reported at roughly USD 38.4 billion).

Prioritise three concrete actions: consolidate leadership and cross‑agency implementation to avoid fragmented rollout; fund sovereign data, cloud and GPU compute so “Make in Vietnam” models can be trained at scale; and close the talent gap with fast, practical upskilling plus strategic hires (MoST targets elite expert recruitment and capacity building).

Training for civil teams should be workplace‑focused and measurable - programmes like the Nucamp AI Essentials for Work syllabus help staff learn promptcraft, tool use and pilot KPIs so ministries can show cost, time and satisfaction improvements on day one.

The upside is large (experts estimate AI could add about USD 79.3 billion, or ~12% of GDP by 2030), but only if governance, funding and people move in concert and pilots are engineered from sandbox to national procurement with clear KPIs and audit trails.

Priority	Next step
Coordination & governance	Create cross‑agency implementation teams to operationalise national AI strategy (avoid fragmented pilots)
Funding & infrastructure	Use NDDF capital to finance sovereign datasets, cloud and GPU compute for local model development (NDDF ≈ USD 38.4B)
Talent & training	Recruit elite experts and scale practical upskilling (e.g., Nucamp AI Essentials for Work) to close operational gaps
Pilots → Scale	Run regulated sandboxes (up to 2 years) per the DTI framework, measure KPIs, then procure for national roll‑out

“Technology is a tool, but humans are the ultimate goal and decisive factor. Even with limitless possibilities, as some perceive, AI remains a man-made product,” he stressed.