Recently, when we talk about AI, we usually refer to generative AI — systems like ChatGPT, Gemini, Claude, or Copilot that can generate human-like text, images, code, or other content. This wave of AI focuses on large language models (LLMs) and foundation models, which are trained on massive datasets and used across industries for automation, creativity, productivity, and decision-making. As artificial intelligence (AI) continues to reshape global industries, PLF Law Firm joined leaders from across Vietnam’s business community at the AImpact Do More with Less by Unlocking Business Efficiency business luncheon, hosted by EuroCham Vietnam and Rikkeisoft in Ho Chi Minh City.
AI is no longer a speculative investment.
It is now a business necessity for companies aiming to remain competitive amid rising costs and economic uncertainty. According to Mr. Quan Ngo – Director of Rikkeisoft, the evolution of AI has moved through three major waves:
- Machine Learning (2000s) – focused on recognizing business patterns from structured data.
- Deep Learning (2010s) – expanded to image, video, and non-structured data.
- Generative AI (2020s) – now enables computers to generate human-like content (text, audio, visuals), led by Large Language Models (LLMs) like ChatGPT.
As we all know, the United States is leading the world in AI investment. Many of the biggest AI companies, like OpenAI, are based there. U.S. tech giants are also investing heavily in computing infrastructure — a critical resource for training and running advanced AI models.
“Since our company is also based in the U.S., we recognize how central this region is to the AI ecosystem.” said Mr. Quan Ngo.
Now looking at Asia, China is the second-largest investor in AI, especially in foundation models. In recent months, we’ve seen many new LLMs coming out of China — for example, models from companies like DeepSeek are gaining a lot of attention. Because of this strong investment, over 50% of Chinese companies are now using AI in their operations.
We’re also seeing strong growth in India and Southeast Asia, where adoption rates are increasing rapidly as companies explore how AI can improve efficiency and scale.
Meanwhile, Europe is a bit behind in this trend compared to the U.S. and China. Although some European companies are investing in building their own language models, and adoption is happening, the overall rate is slightly lower than the global average.
“AI adoption in China is faster than anywhere else,” Mr. Quan noted. “Their companies are building their own models. The rest of the world—including Vietnam—must catch up quickly.”
And for Vietnam, investment in AI is becoming a solution of efficiency.
Vietnamese companies, particularly SMEs, are beginning to test AI use cases. But many lack the infrastructure, policies, or legal safeguards to scale AI responsibly. This presents a key opportunity—and challenge—for legal advisors like PLF.
Yes4All, a global e-commerce brand, showcased how AI radically improved their demand forecasting and campaign optimization:
“We manage 3,000 SKUs across 18 markets. Forecasting and keyword bidding involves 15 million decisions daily. It’s not humanly possible without AI,” said Mr. My Nguyen, CEO of Yes4All.
By using AI to track real-time changes in tariffs, inventory levels, competitor pricing, and customer demand, Yes4All reduced their marketing cost by 30%, saving over $4 million annually.
“It’s not just faster decisions—it’s better ones. AI helps us win against global competitors” Mr. My emphasized.
Meanwhile, Merck Healthcare described their approach of starting small.
“We trained ChatGPT with our internal travel policy SOPs,” shared Mr. Daniel Pham. “Instead of answering repeated HR questions manually, staff now get real-time answers from the AI.”
This “bicycle-before-Mercedes” model offers a blueprint for SMEs: start with targeted AI deployments, build confidence, and scale from there.
Rikkeisoft, a 2,000-person IT services firm, offers a unique look at AI implementation from the inside out. Despite being a tech company, its operations were still highly labor-intensive. The leadership’s conclusion was stark:
“If we don’t adopt AI, we risk losing business,” said Mr. Quan Ngo. “We have shifted from measuring workforce by headcount to measuring by productivity.”
But to approach, there’s no way other than constant learning, experimenting and adapting.
Their approach includes:
- A top-down AI adoption policy enforced quarterly;
- Department-level hackathons to propose use cases;
- Cross-functional teams to incubate, test, and scale successful pilots;
- Investments in AI tools for software generation, automated testing, sales bots, and admin automation.
“We didn’t find any external ‘success story’ to follow,” Mr. Quan Ngo admitted. “We had to experiment, evaluate ROI, and build our own path.”
As businesses embed AI deeper into core functions, the legal challenges are growing:
- Data privacy & governance: AI often requires sensitive customer and business data—raising compliance questions.
- Intellectual property: Who owns the content generated by AI? What happens if it misuses third-party data?
- Labor law: How should companies manage workforce shifts caused by automation?
- Vendor contracting: How to structure AI-related agreements on liability, usage, and model performance?
Through events like AImpact Do More with Less by Unlocking Business Efficiency, companies need to deepen their understanding of the technological, commercial, and legal shifts brought by AI. As businesses accelerate their adoption of AI across Vietnam, PLF is ready to help them do so with confidence, compliance, and clarity.
