Strategic priorities for AI development in Viet Nam
AI is now a core driver of sustainable growth. Viet Nam has clear openings to lead in practical deployments, yet the path from pilots to production still needs work. The decisions made on priorities and resource allocation over the next few years will set the pace for real impact.
Where Viet Nam stands
A new annual report from the Institute of Information Technology, Viet Nam National University, Ha Noi, maps AI education, research, development, and deployment across industry, healthcare, transport, finance and banking, and education. Healthcare currently leads-both in published research and in real implementations. Diagnostic decision support, medical image processing, and healthcare data management are seeing steady adoption. The takeaway: highly structured tasks with measurable outcomes are moving first.
What's blocking scale
- Shortage of experienced AI talent across the stack (data, ML engineering, MLOps, security, evaluation).
- Fragmented data and compute infrastructure that slows experimentation and increases costs.
- Weak links between research, training, and deployment, limiting technology transfer.
- Uneven adoption across sectors and regions; most projects remain pilots with limited spillover.
- Legal framework still being finalised, creating uncertainty in procurement, data access, and model governance.
Policy directions that matter now
Prof. Dr Tran Xuan Tu recommends closing the gap between policy and actual delivery. Here's what that looks like in practice:
- State: Finalise AI regulations and practical guidance to reduce compliance ambiguity. Build shared data and computing infrastructure, and create funding plus innovation mechanisms for SMEs. Encourage sandboxes and public procurement models that reward measurable outcomes.
- Businesses: Treat AI as part of long-term digital transformation, not a set of isolated tools. Invest in data quality, model evaluation, and MLOps to move beyond demos. Prioritise use cases tied to clear KPIs-cost, throughput, accuracy, safety-and iterate quickly.
- Universities and research institutes: Treat AI as knowledge infrastructure. Push interdisciplinary labs, stronger industry partnerships, practice-oriented curricula, and open datasets to accelerate training and technology transfer.
The AI Law creates room for startups to test, learn, and ship products that solve daily problems. Clear rules, accessible datasets, and affordable compute can turn that promise into working systems at scale.
Healthcare: the leading edge of practical AI
Healthcare shows what works: focused problems, strong data foundations, and measurable outcomes. High-impact workloads include:
- Clinical decision support for triage and diagnosis with rigorous human oversight.
- Medical image processing for screening and prioritisation.
- Data integration to reduce administrative burden and surface patient insights.
Success here depends on safety, auditability, and continuous evaluation. Build feedback loops with clinicians, publish metrics, and link models to real clinical outcomes and costs.
From pilots to production: a practical checklist
- Data: Define authoritative sources, set quality thresholds, and document lineage. Secure access with role-based controls and audit trails.
- Compute: Pool capacity through shared clusters or credits; standardise tooling to speed experiments without sacrificing governance.
- MLOps: Version data/models, automate testing, monitor drift, and keep human-in-the-loop pathways explicit.
- Security and trust: Apply risk-based controls, red-team critical systems, and record decisions for traceability.
- Economics: Start with use cases that pay for themselves in 6-12 months and reinvest gains into infrastructure and talent.
Focus areas with leverage
- Specialised AI: Domain-tuned systems for finance, logistics, manufacturing, and public services where structured data is rich and outcomes are clear.
- Edge AI: On-device or near-device inference for low latency, cost control, and data privacy-especially relevant for healthcare, transport, and industrial monitoring.
- Open, shared assets: Sector datasets, evaluation suites, and reference architectures to shorten the time from research to deployment.
With market scale, data breadth, and a young workforce, Viet Nam can build AI ecosystems that compete regionally and internationally. The key is to compound small wins fast: standardise the tooling, share what works, and retire what doesn't.
Next steps for leaders
- Map three high-confidence use cases per department with target KPIs and a 90-day experiment plan.
- Stand up a cross-functional AI review board (engineering, data, legal, security, clinical/domain experts) to unblock decisions weekly.
- Publish internal playbooks: data standards, evaluation protocols, and approval gates that shorten time-to-value.
For policy context, see Viet Nam's National AI Strategy through 2030 here. To accelerate workforce readiness, explore practical AI upskilling paths by job role here.
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