The most important starting point is to determine the correct problem to solve, instead of immediately rushing into selecting the tool. Many organizations today apply AI in a movement: one department tests it itself, another department buys the tool itself, leading to overlap and waste. When AI is deployed without a practical problem, the results are often vague, difficult to evaluate the effectiveness and difficult to convince leaders to continue investing.
According to Mr. Vuong Quan Ngoc, Consulting Director of FPT Digital - FPT Group, businesses should only start with measurable bottlenecks. He emphasized: To apply AI in the right direction, businesses need to start with problems that give clear results.
When there are small victories, businesses have data and confidence to expand to bigger problems. This is an important principle to help businesses avoid spreading and quickly prove their effectiveness.
right start points often lie in repetitive tasks that take a lot of time. AI can automate the reading - classification - extraction of information from text, shorten the time for document processing; synthesize reports much faster than manual methods; or support data analysis in departments without analysts.
When these tasks are solved, businesses will not only save costs but also improve work quality, reduce errors and speed up decision making.
However, if it is only at the task level, it will be difficult for businesses to create a competitive advantage. Therefore, after achieving initial efficiency, businesses need to switch to expanding AI according to the process. This is the period when departments start to link together to create a unified operating flow, from customer care, internal management to forecasting market demand.
When AI is deployed through a continuous process, the value will increase significantly: faster customer service speed, more accurate data, more timely decision making and creative space for human resources will also be expanded.
The highest stage is to include AI in the operating structure, where businesses form standards, measurement systems, implementation playbooks and risk management frameworks. At that time, AI will no longer be a separate tool but will become an operating platform to support businesses to adapt to the market faster. This is also a necessary condition for businesses to move towards high-value problems such as market forecasting, supply chain optimization or building internal digital assistants.
The important thing is that businesses do not need to invest big from the start. The right way is to start with a small problem, prove its effectiveness, then expand with verification. When every step is based on real data, businesses will avoid the situation of "technology waste" and turn AI into growth driver, not a cost burden.
In the context of a rapidly changing market, businesses not only need AI, but also need the right AI strategy. When going right from the start, businesses not only apply technology more effectively but also create a foundation for new momentum in the digital competition stage.
