Vietnam AI Strategy 2030: 10 Brands, One Bold National Plan

TL;DR: Vietnam AI strategy 2030, approved by the Prime Minister on August 28, 2026, targets 10 internationally competitive Vietnamese AI brands, 8 Vietnamese-language AI models, and 100% AI-integrated public services within four years. The plan reshapes how Vietnamese enterprises will need to measure, compare, and trust AI systems going forward.

Vietnam AI strategy 2030 became official policy on August 28, 2026, when the Prime Minister approved the National Strategy on Artificial Intelligence to 2030, with a vision to 2045. The strategy marks a shift from treating AI as a feature bolted onto products and services to treating it as a core capability built into how problems get defined, designed, and run, according to the government announcement reported by VnExpress. Vietnam wants to become one of the leading AI research, development, and value-co-creation hubs in ASEAN and Asia by 2030.

Artificial intelligence (AI) is used throughout this article to mean machine learning and generative systems running in production, not laboratory demonstrations. ASEAN, the Association of Southeast Asian Nations, is the ten-country bloc Vietnam belongs to and the peer group the plan benchmarks against. Both definitions matter, because the targets in Vietnam AI strategy 2030 are written as comparisons against that peer group rather than as absolute scores.

What Does Vietnam's AI Strategy 2030 Actually Target?

The headline numbers in Vietnam AI strategy 2030 are specific. The government wants 10 internationally competitive Vietnamese AI brands, at least 8 Vietnamese-language AI models, and 5 shared national AI platforms in place by 2030. Within the next four years, Vietnam aims to rank among the top three Southeast Asian countries for AI research and development, with 100% of online public services integrated with AI and 100% of administrative paperwork AI-assisted.

Vietnam AI strategy 2030 names nine pillars to get there, starting with building a high-quality AI workforce and spreading basic AI skills across society, including attracting Vietnamese AI experts working abroad and foreign specialists. Other pillars cover data infrastructure, computing and cloud capacity, AI-native products branded "Make in Vietnam," AI in government administration, and AI adoption across priority industries, small businesses, and cooperatives.

Read the target list closely and a pattern appears. Every headline number in Vietnam AI strategy 2030 is countable. Ten brands, eight models, five shared platforms, a top-three ASEAN ranking, and 100% coverage of online public services are all claims an outsider can audit at a fixed date. That is unusual for technology policy, which often relies on adjectives instead. Countable targets create accountability, and they also create exposure, because a number that gets missed is visible to everyone.

The four-year window is short for infrastructure and long for software. Data centres, national platforms, and university programmes run on multi-year cycles. Model releases and public-service integrations can move in months. Vietnam AI strategy 2030 therefore front-loads the slow items, the data and compute foundations, so the fast items have somewhere to run. Enterprises reading the plan should expect infrastructure announcements first and consumer-visible AI features later.

What Does Vietnam AI Strategy 2030 Require in Practice?

A strategy document is not the same thing as delivery. Vietnam AI strategy 2030 sets direction, but direction only becomes outcome through a small number of concrete instruments: budget lines, procurement rules, technical standards, data-sharing mandates, and a body with the authority to say no. Every national AI plan that has produced visible results moved from statement to instrument within its first two years.

Public procurement is the most underrated of those instruments. Government is usually the largest single buyer of software in any economy, so the rules it writes for buying AI shape what vendors build. If tenders under Vietnam AI strategy 2030 require documented training data, published evaluation results, and a named accountable owner, the whole domestic supplier base will build those things. If tenders reward the lowest price for a demonstration, the supplier base will build demonstrations.

Technical standards do the second half of the work. Shared national AI platforms only reduce duplication if the ministries and provinces plugging into them agree on identifiers, file formats, update schedules, and quality thresholds. Without that agreement, five national platforms become five more silos. The data-infrastructure pillar of Vietnam AI strategy 2030 is really a standards pillar wearing a hardware costume.

Coordination is the quiet failure mode. AI policy touches education, science and technology, information and communications, public security, finance, and every line ministry that runs a public service. Nine pillars spread across that many owners need a single sequencing authority, otherwise each pillar optimises locally. Watch for which agency ends up publishing the implementation timetable, because that is the agency actually running Vietnam AI strategy 2030.

Why Does Data Quality Decide the Outcome of Vietnam AI Strategy 2030?

Models sit downstream of data. A Vietnamese-language model trained on a thin, noisy corpus will underperform a similar model trained on a deep, clean one, regardless of how much compute sits behind it. The eight Vietnamese-language AI models named in the plan are therefore a data commitment before they are a modelling commitment.

Vietnamese presents specific challenges. Diacritics carry meaning, so text stripped of accents is degraded rather than merely untidy. Legal, medical, and financial Vietnamese use vocabulary that general web text barely contains. Building usable corpora means licensing published material, digitising archives, and cleaning administrative records, none of which is glamorous and all of which takes years.

Labelling is the cost nobody budgets for. Supervised tasks such as fraud detection, document classification, and entity matching need human-annotated examples written by people who understand the domain. Annotation quality sets the ceiling on model quality. Any serious reading of Vietnam AI strategy 2030 should include a question about who funds and quality-controls annotation at national scale.

Governance decides whether the data can be used at all. Vietnam's personal data rules set conditions on collection, purpose, and transfer, and the penalties for getting it wrong are now material, as covered in our analysis of Vietnam's data protection fines. Meanwhile the legal framework for buying and selling data commercially is taking shape, which we examined in the piece on Decree 314 and Vietnam's data market.

Public registries are the raw material for much of the useful AI. Property and cadastral records, corporate filings, and intellectual property registers all become machine-readable inputs once they are structured, as our reviews of Vietnam's land data and the national intellectual property database describe. Vietnam AI strategy 2030 will advance or stall largely on how quickly registries like these become reliable, current, and accessible.

How Will Vietnam AI Strategy 2030 Change Data Infrastructure?

Two of the nine pillars matter most for anyone building or buying AI in Vietnam: data infrastructure and AI models as the foundation for research, training, testing, and deployment, plus AI compute infrastructure, including cloud capacity and AI data centers. Vietnam AI strategy 2030 treats data as infrastructure, not an afterthought, so demand for verified, structured, and current datasets is likely to grow across banking, government services, and enterprise AI alike.

For the "10 international AI brands" goal in Vietnam AI strategy 2030 to mean anything commercially, Vietnam needs a credible way to measure and compare AI model performance, much like the scrutiny applied to global AI model releases. DataCore already runs AI Rank, a free, real-time leaderboard tracking AI model performance, functioning as exactly the kind of independent benchmark Vietnamese AI brands will need to prove they compete internationally.

Compute is the other half of the foundation. Training and serving models at national scale needs graphics processing units (GPUs), power, cooling, and network capacity, plus the semiconductor supply chain behind them. Vietnam's growing position in chip assembly and testing, discussed in our coverage of Samsung's Vietnam semiconductor plans, matters here, and so does dedicated capacity arriving through partnerships such as the Qualcomm AI hub in Vietnam.

Shared platforms only pay off with interoperability. Five national AI platforms serving many ministries need common authentication, common data contracts, and a versioning discipline so a change in one place does not silently break another. This is ordinary platform engineering, unglamorous and decisive. Organisations that already run disciplined data pipelines will absorb Vietnam AI strategy 2030 with far less friction than those that do not.

Why Do National AI Champions Matter, and What Are the Risks?

Naming a target of 10 international brands is a deliberate choice. Champion-led industrial policy concentrates scarce capital, talent, and political attention on a few organisations large enough to compete outside the home market. It produces visible results faster than diffuse support, and it gives a country recognisable names to negotiate with abroad. That is the logic Vietnam AI strategy 2030 is leaning on.

The model has known failure modes. Concentration can crowd out smaller innovators, tie policy to the fortunes of a handful of firms, and reward proximity to government over product quality. Champions can also settle into domestic monopolies that never actually face international competition, which is the outcome Vietnam AI strategy 2030 tries to avoid by defining the brands as internationally competitive rather than merely large.

Two mitigations usually help. The first is open entry, meaning the champion list stays contestable so a startup that outperforms an incumbent can take its place. The second is independent evaluation, so the label of internationally competitive rests on measured performance rather than self-description. Both mitigations point straight back at measurement.

Open models change the calculus as well. When capable model weights are published openly, a smaller ecosystem can build competitive products without funding frontier training runs itself, a dynamic we covered in our look at open source AI in 2026. For Vietnam that is leverage: fine-tuning strong open models on high-quality Vietnamese data may be the fastest route to several targets in Vietnam AI strategy 2030.

How Can Progress Against Vietnam AI Strategy 2030 Be Measured?

Measurement is where most national plans turn vague. Counting brands is easy; deciding whether a brand is internationally competitive is not. The same applies to 100% AI-integrated public services, which could mean a chatbot on a portal or an end-to-end automated decision with a full audit trail. Definitions written now determine whether the 2030 review of Vietnam AI strategy 2030 is credible.

Split the indicators into leading and lagging. Lagging indicators are the headline targets themselves: brands, models, platforms, service coverage, ASEAN ranking. Leading indicators are the things that move first, such as the number of published Vietnamese-language benchmark datasets, the share of public tenders requiring documented evaluation, annotated corpus volume, and available accelerator capacity.

A workable public scoreboard for Vietnam AI strategy 2030 would report a short list every quarter: model performance on Vietnamese-language tasks, uptime and adoption of the shared national platforms, the count of registries published in machine-readable form, graduate and reskilled-worker numbers entering AI roles, and the proportion of public services where an AI component is genuinely used rather than merely installed.

Cadence matters more than precision. An imperfect indicator published every quarter drives better behaviour than a perfect indicator published once in 2030, because it lets course corrections happen while there is still time to correct. Independent third-party measurement adds credibility, which is why leaderboards and benchmarks count as infrastructure rather than marketing.

What Should Vietnamese Enterprises Do About Vietnam AI Strategy 2030 Now?

Start with an honest data inventory. List the datasets your organisation actually controls, note who owns each one, how often it updates, what its known error modes are, and whether you hold the legal right to use it for model training. Most organisations discover the real blocker is not access to models, it is the state of their own records.

Pick two or three use cases with measurable value and bounded risk. Document classification, entity matching, forecasting, and customer-service triage are common starting points because success and failure are both easy to see. Write down the success metric before building anything, and write down what result would make you stop.

Build an evaluation harness before scaling. A held-out test set drawn from your own data, refreshed regularly, is worth more than any vendor benchmark. Without one, teams ship plausible-sounding output that quietly degrades, the pattern described in our piece on AI slop in Vietnamese enterprises. Evaluation is also how you tell whether a Vietnamese-language model built under Vietnam AI strategy 2030 beats a general international one on your task.

Invest in people ahead of tools. One or two colleagues who genuinely understand evaluation, data quality, and deployment will outperform a larger team using AI features they cannot inspect. Our overview of Vietnam AI skills to 2030 sets out which capabilities are becoming scarce.

Do not defer security and compliance. Model inputs and outputs are data flows subject to the same rules as everything else, retention and cross-border transfer included. Longer-dated cryptographic risk deserves a line in the plan too, a topic explored in our article on post-quantum cryptography in Vietnam.

How Does the Talent Pipeline Fit Vietnam AI Strategy 2030?

The first pillar listed is people, which is the right order. Vietnam AI strategy 2030 pairs building a high-quality AI workforce with spreading basic AI skills across society, and it names two supply channels explicitly: Vietnamese AI experts working abroad, and foreign specialists.

Diaspora recruitment works when the offer is a real research or engineering environment, not only a salary. Senior people return for interesting problems, access to data and compute, and colleagues at their own level. That means the workforce pillar depends on the infrastructure pillars landing first, which is a sequencing constraint rather than a nice-to-have.

Domestic competition for the same people is intense, and compensation reflects it, as our review of Vietnam tech talent salaries in 2026 shows. Public-sector and academic employers rarely win bidding wars, so they need non-cash advantages: unique datasets, mission, publication freedom, and stable long-horizon funding.

Breadth matters as much as depth. One hundred percent AI-integrated public services implies tens of thousands of civil servants who can use, question, and escalate AI output competently. That is a vocational training problem at national scale, and it is the part of Vietnam AI strategy 2030 most likely to be underfunded because it produces nothing announceable. Dedicated research capacity helps on the depth side, which is why arrangements such as the Vietnam AI research and development hub carry weight beyond their headcount.

How Do Other Markets Structure National AI Plans?

National AI plans around the world reuse a small set of building blocks: a research funding programme, a compute or cloud provision scheme, a data-access regime, a skills programme, a regulatory or ethics framework, and a public-sector adoption mandate. What differs between countries is emphasis and sequencing rather than the components themselves.

Three broad archetypes recur. Research-first plans fund universities and laboratories and wait for spillover into industry. Adoption-first plans push AI into public services and regulated sectors to create guaranteed demand. Champion-first plans back a small number of firms to compete abroad. Most real plans blend all three, weighted by whatever the country already has.

Vietnam AI strategy 2030 reads as adoption-first and champion-first together, with research support alongside. The 100% public-service target manufactures demand, the 10-brand target manufactures suppliers, and the data and compute pillars try to ensure those suppliers can actually deliver. That combination is coherent, and it puts unusual weight on the state as both customer and standard-setter.

The transferable lesson from elsewhere is unromantic. Plans that published implementation timetables, named owners, and recurring public metrics generally outperformed plans with larger budgets and vaguer accountability. The scarce resource is rarely money or ideas; it is follow-through.

What Does the Data Tell Us About Vietnam AI Strategy 2030?

Set the announced targets side by side and the shape of the task becomes clearer. Each target implies a different kind of work, a different owner, and a different lead time, which is why treating Vietnam AI strategy 2030 as one single programme is misleading. The table below groups every announced figure with the capability it mainly depends on.

Two observations follow from that table. First, several lines are infrastructure or process work with lead times measured in years, so slippage there is the leading risk. Second, only the ranking lines depend on what other countries do, which means the plan can be largely delivered through domestic execution while still being judged comparatively against ASEAN peers.

What the numbers do not say is informative too. The targets published so far include no public budget figure, no named implementation agency, and no interim milestones for 2027 or 2028. Those three items are the ones worth watching for next, because they are what converts a strategy into a plan.

What Comes After Vietnam AI Strategy 2030 Through 2045?

The vision behind Vietnam AI strategy 2030 extends well past its 2030 milestones. By 2045, the longer arc aims to put the country among the top 10 nations in Asia for AI research, development, and mastery, with AI positioned as a strategic driver of productivity, innovation capacity, and national value creation. Vietnamese officials frame this explicitly as a path out of the middle-income trap and toward high-income developed status.

Getting there requires a functioning AI-native business ecosystem capable of competing regionally and globally, not just a handful of flagship projects. The enterprises building toward Vietnam AI strategy 2030's goals now, in banking, insurance, government services, and data infrastructure, are the ones positioned to benefit first as funding, workforce, and platform commitments land over the next four years.

Read as a sequence, the 2030 targets are the input and the 2045 position is the output. Brands, models, and platforms built this decade become the installed base the following two decades compound on. That is why the unglamorous items, corpora, standards, registries, and trained people, deserve more attention than the headline brand count in Vietnam AI strategy 2030.

What Is the Key Takeaway from Vietnam AI Strategy 2030?

One sentence covers it: the strategy converts AI from a procurement category into national infrastructure, and infrastructure gets judged on reliability rather than novelty. For enterprises, the practical consequence is that data quality, evaluation discipline, and skilled people are now competitive assets instead of back-office hygiene.

The second takeaway is about verification. Ten internationally competitive brands is a claim, and claims need independent checks. Whoever supplies credible, current, comparable measurement of AI performance in Vietnam will shape how progress under Vietnam AI strategy 2030 gets understood, and that role is open right now.

Frequently Asked Questions

What are the main targets of Vietnam's AI Strategy to 2030?

Vietnam AI strategy 2030, approved August 28, 2026, targets 10 internationally competitive Vietnamese AI brands, 8 Vietnamese-language AI models, 5 national shared AI platforms, and 100% AI-integrated online public services within four years.

How many Vietnamese AI brands does the strategy target by 2030?

The strategy targets 10 internationally competitive Vietnamese AI brands by 2030, alongside at least 8 Vietnamese-language AI models and 5 shared national AI platforms.

When was Vietnam's national AI strategy approved?

The Prime Minister approved the National Strategy on Artificial Intelligence to 2030, with a vision to 2045, on August 28, 2026. VnExpress reported the approval on August 30, 2026.

How many pillars does Vietnam AI strategy 2030 contain?

Nine pillars. They cover the AI workforce plus society-wide AI skills, data infrastructure, computing and cloud capacity, AI models, AI-native Make in Vietnam products, AI in government administration, and AI adoption across priority industries, small businesses, and cooperatives.

Why does data infrastructure matter so much to the strategy?

Model quality is capped by data quality. Vietnamese-language models need deep, clean, well-labelled corpora, and AI-integrated public services need registries that are structured, current, and legally usable. Without that foundation, compute and funding alone will not produce competitive models.

What should a Vietnamese company do first to prepare?

Run a data inventory covering ownership, refresh frequency, error modes, and legal usage rights. Then pick two or three bounded use cases, define the success metric in advance, and build a held-out evaluation set from your own data before scaling anything.

What happens to Vietnam's AI ambitions after 2030?

By 2045, Vietnam aims to rank among the top 10 countries in Asia for AI research, development, and mastery, using AI as a strategic driver to move beyond the middle-income trap toward high-income developed status.

How can DataCore support Vietnam's national AI strategy?

DataCore runs AI Rank, a free real-time leaderboard tracking AI model performance, giving Vietnamese businesses and policymakers an independent way to measure progress under Vietnam AI strategy 2030.

As Vietnam AI strategy 2030 pushes every sector toward AI-native operations, independent measurement becomes as important as the technology itself. Track how Vietnamese and global AI models actually perform on DataCore's AI Rank leaderboard, updated in real time. For broader data infrastructure needs, see DataCore's full services catalog.

Sources

• VnExpress, "Viet Nam dat muc tieu co 10 thuong hieu AI quoc te nam 2030," Vu Tuan, August 30, 2026. Read the source article.