2024 marks a historic turning point for the AI market in Vietnam. According to the Government Electronic Newspaper, within just one year, total investment in domestic AI companies skyrocketed from $10 million (2023) to $80 million (2024), an unprecedented 8x growth. This figure reflects the strong appeal of AI to both domestic and international investors.
Beyond financial investments, Vietnam's AI market is witnessing a surge of large-scale projects: data centers, AI Factories, and AI Campuses with total investments reaching billions of dollars are being launched in major cities like Hanoi, Ho Chi Minh City, and Da Nang. Global tech giants such as Microsoft, Google, Amazon Web Services, Qualcomm, and Dassault Systèmes have also chosen Vietnam as a strategic destination to build AI research, development, and operations centers for the Asia region.
At the same time, the legal framework for AI is being rapidly completed. Vietnam has enacted the AI Law and implemented a national AI development strategy through 2030, aiming to become one of Southeast Asia's top three AI hubs. The government is also proactively promoting incentives and support policies for businesses investing in AI research, development, and application across all sectors: finance, banking, healthcare, manufacturing, e-commerce, smart cities, and more.
Alongside these positive signals, the AI investment wave in Vietnam also poses significant challenges for businesses and managers. In reality, many companies have burned money on AI without seeing clear results, and some have paid a high price for failures due to lack of strategy, operational foundation, or deploying AI without linking it to specific business problems. AI investment in Vietnam presents great opportunities, but only businesses with long-term vision, clear strategy, and a solid operational foundation can turn AI into a true competitive advantage.
AI is becoming a golden keyword in the tech market, appearing everywhere from forums and conferences to industry reports and strategic business meetings. Many leaders feel pressured to do something with AI to avoid falling behind or to prove to shareholders and partners that their company is keeping up with global trends. As a result, many AI projects are launched hastily, lacking thorough research and not tied to specific business objectives.
According to a survey by the Institute of Information Technology, the biggest challenge for Vietnamese AI businesses today is the lack of high-quality human resources, standardized data, and synchronization between development and application. When investing in AI just to follow trends, companies easily fall into a cycle of endless experimentation without a long-term strategy, leading to wasted resources and missed opportunities to create real value.
A common mistake among business leaders is placing too much hope in AI, seeing it as a magic wand that can automate all processes, dramatically boost productivity, or create business breakthroughs in a short time. In reality, AI only works effectively when integrated into a structured operating system, with quality input data, clear processes, and context.
If a business hasn't standardized its processes, built a data-driven culture, or clearly defined AI application goals, deploying AI only adds complexity, increases management costs, and may even introduce new risks. AI cannot magically transform a chaotic organization into an efficient one overnight. In fact, AI amplifies what already exists in the system. If your processes are messy, AI will make that mess more apparent. Sometimes, AI even creates a sense of "fake perfection," masking real problems within the business.
Many businesses make the mistake of thinking that simply investing in modern AI technology will solve all problems. However, AI cannot fully replace humans, especially in strategic decisions, handling complex situations, or driving innovation.
If a business doesn't standardize its processes, clarify roles and responsibilities, and build a transparent data culture, AI will remain a fragmented tool, serving only individuals or small groups without creating overall value. Investing in AI while neglecting people and processes is like building a house on a weak foundation. It will soon reveal its weaknesses and struggle to develop sustainably.
A deeper reason why many businesses burn money on AI without seeing results is the lack of a System of Work, a platform that unifies tools, processes, and people. Without a clear working system, AI only solves small, isolated tasks and fails to drive overall change in operations and decision-making.
AI tools that are not integrated into real workflows remain fragmented, lack context, and cannot reach their full potential. Conversely, when a business builds a solid System of Work, AI becomes a powerful catalyst for optimizing processes, increasing efficiency, and creating sustainable value for the organization.
One of the most common mistakes is investing in AI just because the market is hot, competitors are doing it, or being swept up in new tech trends without truly understanding what problem needs to be solved. Many leaders purchase expensive AI solutions without defining specific business goals, having value measurement criteria, or knowing how AI will improve operations or growth.
As a result, AI projects often remain at the pilot stage, fail to make real impact, or cannot be scaled across the business. Sometimes, these projects die out after a while, causing significant waste of time, money, and resources.
AI is only truly intelligent when fed with high-quality, comprehensive data and clear context. However, many businesses overlook this, leading to AI working with fragmented, unstandardized data that doesn't reflect real operations.
For example, if customer data only records transactions without reasons, context, or exceptions during processing, AI cannot make accurate forecasts or recommendations. Poor input data leads to incorrect AI outputs, resulting in bad decisions and wasted investment. This is a major reason why many AI projects fail from the start.
Many businesses have digitized steps in their workflows, but key decisions and critical exchanges still happen outside the system, via email, in-person meetings, or verbal discussions. This breaks the workflow, making it impossible for AI to see the whole process for analysis, learning, or optimization.
When important information isn't systematically recorded and stored, businesses struggle to control risks, assess effectiveness, and improve processes. AI then becomes just a minor support tool, unable to realize its full potential in enhancing operational efficiency.
Another trap is not clearly defining who is responsible at each stage of AI deployment. When AI makes a recommendation, who approves it? If AI detects an anomaly, who investigates and handles it? If AI makes a wrong decision, who is ultimately accountable?
Without clarifying these roles from the start, businesses fall into a gray area of responsibility. When incidents occur, departments easily blame each other, risks go unaddressed, and internal disputes increase. This not only affects operational efficiency but also erodes trust in AI projects within the organization.
Many businesses, eager to keep up with trends, deploy a slew of AI tools across various departments and fields without an overall integration strategy. The result is a fragmented tech ecosystem, disconnected tools, fragmented data, and high maintenance and operational costs.
Instead of focusing on solving the biggest operational bottlenecks, businesses spread resources across too many small AI projects, leading to low effectiveness and difficulty in creating sustainable value.
In summary: To avoid falling into AI budget waste traps, business leaders must clearly define business goals, build a quality data foundation, standardize processes, clarify responsibilities, and have an overall technology integration strategy. Only then can AI truly become a lever for organizational value creation.
When it comes to AI investment, many businesses immediately think of buying new technologies, hiring AI experts, or building complex models. However, according to Atlassian research and BiPlus's practical experience, the decisive factor is a less-noticed foundation: the System of Work - the organization and operation system of the business.
You can learn more about this topic in the article AI-powered System of Work: Why Does AI Only Work When Embedded in How Businesses Operate?.
A System of Work is not just a set of software tools or a workflow. It's how a business organizes, operates, and manages all work, from strategy building, planning, and task execution to knowledge and experience storage and sharing.
In other words, the System of Work is the backbone that ensures all activities in the business are transparent, clearly structured, and easily controlled. It ensures everyone knows what they're doing, why they're doing it, who is responsible at each stage, and that all important information is systematically stored and shared.
Enables AI to Access Standardized Data, Context, and Processes
AI only reaches its full potential when fed with quality data, clear context, and standardized processes. The System of Work helps businesses organize data, record decisions, store knowledge, and standardize workflows. This allows AI to easily access, analyze, and make accurate, useful recommendations for each specific situation.
Increases Visibility, Control, and Measurement of AI Effectiveness
When all activities are recorded and tracked in a unified system, businesses can easily observe the entire workflow, control bottlenecks, and measure AI effectiveness at each operational stage. This helps leaders make data-driven decisions instead of relying on intuition.
Minimizes Risks When AI Goes Wrong
The System of Work clarifies the roles and responsibilities of each individual and department in AI operations. When incidents or anomalies occur, businesses can easily trace, handle, and quickly resolve them, minimizing risks and avoiding unnecessary internal disputes.
Optimizes AI Investment Costs
Instead of spreading investment across many small, isolated AI tools, the System of Work helps businesses focus resources on real operational bottlenecks. As a result, investment costs are used more effectively, delivering clear and sustainable value to the organization.
Many BiPlus clients have proven the effectiveness of standardizing their System of Work before deploying AI. They started by building a culture of documentation, knowledge storage, workflow standardization, and clearly defining responsibilities for each position. Once this foundation was solid, integrating AI into operations became much easier, more effective, and less risky.
The result is that AI is no longer a fragmented tool but becomes an indispensable part of the operating system, helping businesses accelerate, optimize costs, and create real value from technology investments. Therefore, AI investment will only truly succeed when built on a solid, transparent, and well-structured operational foundation.
As businesses increasingly focus on digital transformation and AI-driven operations, building a modern, transparent, and efficient System of Work is vital. Atlassian Teamwork Collection is a comprehensive solution designed to fully meet this need, helping businesses not only optimize workflows but also maximize the practical power of AI.
The AI wave in Vietnam is opening up many great opportunities, but also carries risks of budget waste if leaders lack the right strategy. Investing in AI is not just about buying technology, but about investing in the System of Work foundation so that AI can truly create sustainable value.
BiPlus is always ready to accompany Vietnamese businesses on the journey to build a modern System of Work and deploy AI safely and effectively with the Atlassian Teamwork Collection solution.
If you want to optimize costs, increase AI investment effectiveness, and build a solid operational foundation, contact BiPlus for detailed consultation!
