Strategic priorities for AI development in Viet Nam: from policy to production
AI is a core driver of sustainable growth. Viet Nam has a real opening here, but progress depends on choosing clear priorities and putting resources where they create compounding value.
The 2025 annual report on AI in Viet Nam from Viet Nam National University, Ha Noi, maps the current state across education, research, development, and deployment in key sectors: industry, healthcare, transport, finance and banking, and education. Healthcare is furthest along, especially in diagnostic decision support, medical image processing, and data management.
What's holding things back? Most deployments are still pilots with limited spillover. The big bottlenecks are talent shortages, fragmented data and compute, weak links between research, training, and production, and a legal framework that is still being finalized. The upside: the AI law and sandbox mechanisms are opening the door for startups to ship real products.
Where policy, business, and academia can move the needle
- State (policy and public infrastructure): finalize clear legal guidelines, enable safe experimentation (sandboxes), and build shared data and compute infrastructure. Prioritize financial support for SMEs and public-private R&D programs.
- Businesses: treat AI as a long-term pillar of digital transformation, not a short-term tool. Invest in data foundations, MLOps, and measurable outcomes.
- Universities and research institutes: position AI as knowledge infrastructure across disciplines. Tighten the loop between research, training, and deployment to grow national science and technology capacity.
Sector focus with near-term ROI
- Healthcare: expand clinical decision support, medical imaging triage, and secure data platforms; prioritize validation, auditability, and privacy-by-design.
- Industry and transport: predictive maintenance, computer vision for quality control and safety, scheduling optimization, and edge inference where connectivity is limited.
- Finance and banking: risk scoring, fraud detection, KYC automation, and customer service copilots with strong controls and traceability.
- Education: personalized learning support, automated assessment aids, and AI-assisted content creation with plagiarism and bias checks.
National priorities to unlock scale
- Data infrastructure: shared public datasets, secure data-sharing agreements, data trusts, and incentives for high-quality labeling. Establish common schemas and APIs to avoid one-off integrations.
- Compute access: national GPU clusters, preemption-friendly schedulers, cloud credits for SMEs and universities, and standardized inference gateways to cut hosting costs.
- Talent pipelines: industry-aligned curricula (MLOps, data engineering, evaluation), funded apprenticeships, and cross-appointments between universities and companies.
- Research-to-production bridges: translational labs, challenge grants tied to deployment, open reference implementations, and domain-specific benchmarks.
- SME enablement: pre-trained models, API catalogs, model evaluation templates, and technical vouchers for pilots that meet compliance basics.
- Edge AI: support toolchains (ONNX, TensorRT), quantization-aware training, and offline-first design for factories, clinics, and transport hubs.
- Safety and governance: privacy engineering, consent management, dataset and model lineage, and independent evaluation. Consider frameworks like the NIST AI RMF for practical risk controls (NIST AI RMF).
For IT and development teams: a practical playbook
- Start with data: build reliable pipelines, a feature store, and quality checks. Decide upfront how you'll handle PII, access control, and retention.
- Choose the right model path: fine-tune vs. prompt-engineer vs. train from scratch. Use domain-specific models where possible and measure with task-level metrics, not vibes.
- MLOps and delivery: set up CI/CD for models, model registries, evaluation harnesses, canary releases, and rollback plans. Instrument monitoring for drift, cost, and latency.
- Optimize inference: batch where you can, cache expensive calls, use quantization/pruning, and route requests by workload (CPU/GPU/edge) to control unit economics.
- Security and compliance: redact sensitive data, isolate secrets, scan model artifacts, and log all decisions with audit trails. Red-team models for prompt injections and data exfiltration.
- Pilot to scale: run small, high-signal pilots with clear KPIs and handoff plans to operations. Kill what doesn't work fast; scale what does.
Measuring real progress
- Time-to-first-deployment and time-to-iteration
- Unit economics per prediction or token, and infra utilization
- Model accuracy on live data, drift rates, and alert MTTR
- Share of workloads using shared data/compute platforms
- Talent metrics: trained engineers, retained hires, and industry-academic projects shipped
Why Viet Nam can win with specialized AI
The market is large, data is rich, and the workforce is young. Specialized applications that solve real business and social problems-plus edge AI where connectivity is inconsistent-can give Viet Nam a distinct identity with regional and global reach.
If you're building or upskilling teams, consider structured paths by role and domain (AI courses by job). For policy context and international benchmarks, the OECD AI Observatory is a helpful reference (oecd.ai).
The moment favors focused execution: get the legal and infrastructure basics in place, tie research to deployment, and make AI a long-term pillar of transformation. That's how pilots become production-and production creates compounding advantage.
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