AI Product Development & Innovation in Vietnam

Vietnam's AI Law, effective March 2026, introduces a risk-based classification system (high, medium, low risk). High-risk AI systems require mandatory pre-market conformity assessments and human oversight. We help you assess which classification applies to your use case and prepare the necessary governance documentation before enforcement begins.

Vietnam's Personal Data Protection Law (effective January 2026) requires explicit prior consent for data processing, 72-hour breach notification to the Ministry of Public Security, and impact assessments for cross-border data transfers. The Cybersecurity Law also requires 24-month local data storage for Vietnamese user data. Our implementation includes compliance checkpoints for all these requirements.

Yes. Given that only 15-20% of Vietnam's workforce has business-level English, we deliver all training with Vietnamese-language materials and bilingual facilitators. Hands-on exercises use Vietnamese-language examples and locally relevant case studies.

Vietnam offers several incentive mechanisms: preferential CIT rates of 10% for up to 15 years for high-tech projects (vs. standard 20%), the Investment Support Fund (Decree 182/2024) with cash grants for qualifying AI investments, and the NIC Digital Talent Development Program for workforce training. We help you document your investment to qualify for applicable incentives.

While FPT (USD 2.47B revenue) and VinAI offer strong local capability, Pertama Partners brings cross-ASEAN implementation experience with deep regulatory expertise across multiple jurisdictions. We provide structured methodology validated across Southeast Asian markets, combined with Vietnam-specific compliance guidance that reflects the latest AI Law, PDPL, and Cybersecurity Law requirements.

Traditional product analytics teaches you to use tools (Amplitude, Mixpanel) to track events and build dashboards. This AI-focused training teaches you to use AI to predict user behavior, automate insight discovery, and build intelligent product features. You'll learn behavioral segmentation (AI clustering), impact forecasting (AI prediction), and feedback synthesis (AI NLP) — capabilities beyond traditional event tracking.

Minimum: (1) Some form of event tracking (even basic GA4 or Mixpanel free tier), (2) User feedback channels (support tickets, NPS surveys, or reviews). Ideal: Product analytics platform (Amplitude, Heap, Mixpanel) with historical data. During Discovery, we'll assess your current setup and recommend quick-win improvements to maximize training value. Even basic event data enables powerful AI insights.

Yes! Module 4 is designed for non-AI-native products adding intelligent capabilities. You don't need a team of ML engineers. We'll show you how to: (1) Use pre-built AI APIs (OpenAI, Google AI) to add smart features quickly, (2) Build simple recommendation engines using collaborative filtering, (3) Partner with AI vendors for specialized capabilities. Goal: embed AI in your product to improve UX, not become an AI research company.

Critical concern addressed throughout the training. We teach AI feature validation: (1) Use AI analytics to identify high-frequency pain points first, (2) Prototype AI features using Wizard of Oz testing (manual behind the scenes), (3) Launch AI features to small user segments with opt-in/opt-out, (4) Measure adoption and satisfaction before full rollout. AI should solve real user problems, not be AI for AI's sake.

Highly relevant for both. B2B SaaS examples in the training: AI usage insights identifying expansion opportunities, AI onboarding optimization reducing time-to-value, AI feature adoption tracking for CSM prioritization, AI feedback analysis from customer calls and support tickets. Consumer product examples: AI personalization, recommendation engines, smart notifications. We customize content to your business model during Content Customisation.