2026 AI Trends: Choosing the Right Path for Vietnamese Enterprises

26/08/2026

Lessons from a 'Stillborn' Project in the Dry Season

A year ago, I sat across from the Operations Director of a major supermarket chain in Binh Duong. He wanted to immediately deploy a fully automated AI warehouse monitoring system based on the world's largest model at the time. We were enthusiastic. I promised a future without human inventory checks.

The result? After three months, the project was halted. Not because the technology failed, but because the cloud operating costs to process 24/7 video data from 50 warehouses made the finance team 'smoke'. The system was too complex; no one in the internal IT team knew how to fix it when errors occurred. We won technically but lost operationally.

That lesson changed my consulting approach from 2025 to 2026. The narrative of 'bigger is better' is gone. Now, when discussing AI trends for Vietnamese enterprises, I ask: What are you willing to trade for what? Every choice is a fork in the road. Choose wrong, and you will find yourself in that meeting room again.

Fork in the Road #1: Multimodal or Single-Modal?

By 2026, the boundaries between data types have blurred. You no longer choose between a chatbot or a security camera. Modern enterprise AI systems are moving towards Multimodal capabilities—the ability to read text, view images, and listen to voice simultaneously.

However, do not rush. Integrating multimodal systems requires extremely clean data infrastructure. Imagine an instant noodle factory. A camera sees a torn package (image), a sensor records rising humidity (numerical data), and a worker complains about machine noise (auditory). A single-modal system would only flag a machine error. A Multimodal system connects these three points to predict: 'The machine shaft will fail in 4 hours due to high humidity and excessive friction.'

The trade-off here is data preparation time. If you lack basic data storage processes, jumping straight into Multimodal is self-defeating. AIVISION has partnered with food industry clients like Meat Deli and breweries to clean data before deploying these models. We found that about 40% of project time must be dedicated to 'cleaning up' legacy data. Is it a headache? Yes. But it is the price of accuracy.

Fork in the Road #2: Autonomous Agents or Passive Virtual Assistants?

Chatbots answering customer questions are outdated. This year's trend is Agentic AI—AI agents capable of planning and executing actions independently. Instead of asking 'How much did we sell today?', an Agent automatically checks inventory, detects low stock, and places a purchase order immediately, pending your approval.

This is a game-changer for retail chains in Vietnam, Thailand, or the Philippines. But be careful. Empowering AI to act autonomously means you bear responsibility when it makes a 'wrong decision'. I once saw an Agent automatically cancel a VIP customer's order because it misinterpreted a new promotion policy.

The safest approach is 'Human-in-the-loop'. Let AI handle 80% of the work, but keep the approval button with humans for critical decisions. Do not try to eliminate humans entirely in the first year. That is a path to chaos, not automation.

Fork in the Road #3: Global Cloud or On-Premise?

This is the question that keeps many CIOs awake at night. Large models running on the cloud are powerful and continuously updated. However, cost and latency are major issues, especially for factories in Vietnam or store chains in Mexico where internet connectivity is unstable.

In 2026, Small Language Models (SLMs) are smart enough to run directly on your internal servers. They consume less power, process faster, and most importantly, data never leaves the company premises.

This decision depends on data sensitivity. If you are a pharmaceutical company like Gene Solutions, keeping internal research data secure is mandatory. On-premise is the only choice. Conversely, if you are an e-commerce startup needing access to the latest features daily, the cloud remains the optimal choice. Do not try to impose one solution on all. Look at your specific budget and security risks.

New Technology or Old Processes?

There is a stark truth: You can buy the best technology in the world, but if your business operations still run on 2010-style processes, AI is just an expensive decoration.

I have seen many companies invest billions in custom AI software, yet employees still manually enter data into Excel and copy it to the system. AI cannot save a nonsensical process. Before discussing AI trends, review your processes. Are they logical? Are they necessary?

Do not let new technology mask old loopholes. Preparing for AI is truly about preparing people and processes. At AIVISION, we often spend the first week just observing actual workflows, without discussing technology. Only when we understand the real 'pain points' can we select the right tools.

Frequently Asked Questions

Do small businesses need to deploy AI immediately?

Not necessarily. Start with existing AI tools to solve specific problems, such as customer support or sales report analysis. Only invest in complex systems when your scale and data volume are large enough to generate real value.

How much does deploying an AI Agent cost?

There is no fixed number. It depends on the complexity of the processes to be automated. A simple Agent might cost only a few million VND for operating expenses, while a complex supply chain management system could cost billions. Calculating ROI before starting is crucial.

Is on-premise more secure than the cloud?

In terms of data, on-premise allows you absolute control over information. However, system security depends on your internal IT team. Without security experts, internal servers can also be attacked. Safety is a combination of storage location and protection protocols.

AIVISION helps enterprises turn AI into working systems. Explore our enterprise AI solutions, read more on the AIVISION blog, or talk to our team about your own use case.