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- Figures presented at AWS Cloud & AI Day Hanoi indicate that 75,000 Vietnamese businesses began using AI in the past year.
- The share integrating AI into operations remains far below the share experimenting with it, so implementation—not tool awareness—is the real challenge.
- A good pilot needs one defined task, a before-and-after measure and a business owner.
AI is arriving quickly, but not yet deeply embedded
About 75,000 Vietnamese businesses began using AI in the past year, bringing the total close to 245,000, according to AWS and Strand Partners figures presented at AWS Cloud & AI Day Hanoi and reported by VnExpress. The survey puts current AI adoption at 26%, up from 18% a year earlier.
The more revealing number sits behind that headline: 61% are still exploring or testing, while only 8% have integrated AI into processes, business functions or operations. That does not mean AI is ineffective. It means the hard work begins after the demo—putting a tool alongside the right data, permissions and working habits.
- What happened: business AI use is growing quickly.
- Who is affected: operations leaders, IT, sales, customer care and HR teams asked to turn tests into outcomes.
- What to do now: review current experiments and choose one clear task for a real workflow.
When AI has reached many teams but not the workflow, the missing piece is rarely another tool.
Trying a tool is not the same as implementing work
An employee using a chatbot to summarise a document, draft an email or translate copy can already gain time. A business begins to see operational value when that answer connects to a real next step: drafting a quotation, classifying an enquiry, locating a record, compiling a report or checking data before it reaches an owner.
The gap is usually data and accountability. When inputs are scattered across email, personal files and disconnected systems, AI can only help one person at a time. When nobody owns the workflow, nobody can define a good result or decide where an error should go.
- Do not load every internal document into an AI system merely to see what it can answer.
- Do not call a trial successful before measuring time, rework and error.
- Do not leave technical teams alone to decide a business process.
Choose a small task that is already expensive
The first task does not need to be dramatic. A service company might classify contact forms and prepare a reply for sales to approve. An operations team might create a standard weekly summary from selected sales data. An HR team might sort CVs against agreed criteria for a recruiter to review.
The pattern is the same: a bounded input, a checked output and an existing task repeated often enough for saved time to matter. Recent Vietnamese reporting has focused increasingly on moving AI from trial to operations; that is a more useful question than comparing a list of models or chatbots.
- Write down who does what, how long it takes and where errors occur.
- Use the minimum dataset needed for the task.
- Set a review date within weeks to keep, refine or stop the pilot.
Record four numbers before claiming value
Before a pilot, record average handling time, rework, exceptions and the cost of the people doing the work. Afterwards, compare the same kind of work. A quickly generated draft that takes longer to check is not necessarily an improvement.
Track whether people actually adopt the new flow, too. AI does not create value because it has been purchased or connected by API. Value appears when work finishes faster at the same quality, or when a team gains time for judgement and customer conversation.
- Time from request to completion.
- Rate of material revision or rejection.
- Cases handed back to a person.
- Cost per task, including review time.
Data and working habits still decide the outcome
Many pilots stop not because the model is unusable, but because users do not trust it, old data is inconsistent or the new flow adds an unowned step. Before connecting AI to a CRM, inbox or internal software, agree which data may be used, which actions need approval and who reviews the audit trail when something goes wrong.
The next move for Vietnamese businesses is not to chase every new feature. It is to choose a genuine bottleneck, make it measurable and expand only when evidence supports it. The 8% figure is not only a gap; it is the work ahead for teams that want AI to become operating capability.
- Start with read access and drafts before allowing external sends or data changes.
- Bring a business owner into the work from day one.
- Keep exceptions as input for the next iteration rather than quietly abandoning the tool.
FAQ
Frequently asked questions
Where should a business start an AI pilot?
Start with repeated work that has clear input and a person who can check the output. Enquiry classification, report summaries and draft preparation are safer first cases than high-consequence processes.
Why has AI use not produced a visible result yet?
Scattered use often helps individuals only. Operational value needs AI connected to a defined work step, appropriate data, limited permissions and a before-and-after measure.
Should AI send emails or update the CRM autonomously from the start?
No. In the first pilot, let AI read, classify and draft. Actions that communicate externally or change data should require approval from an accountable person.
References
Sources used in this guide
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