More than 1,000 business leaders, technology specialists, startup founders and investors gathered in Ho Chi Minh City on 18 August for “The Future of Artificial Intelligence: Chapter 4”, a conference focused on moving AI from experimentation into real business operations.
A demo can be built in a day. Production is the hard part.
Vulcan Zhao, AI CTO at A1P, told the conference that the speed of creating AI prototypes has increased dramatically. In some engagements, a team can spend an hour understanding a client workflow in the morning and deliver a functioning demonstration before the end of the day.
The challenge begins when that demonstration must become a dependable production system. Enterprises need to define inputs and outputs, error handling, exceptional cases, human oversight and connections to existing infrastructure. Because the same request can produce different results, reliability becomes one of the biggest barriers to deployment at scale.
Zhao also cautioned against products that promise an entire market study, competitor analysis and marketing plan from a single instruction. Each stage still requires appropriate data, research, verification and design. Overpromising at the prototype stage is often an early warning sign that an AI project may not survive real-world use.
An AI prototype may appear in a day; real transformation begins when it can operate reliably, profitably and at scale.
Key takeaway from Vulcan Zhao’s remarks
Enterprise AI readiness is about people and governance
Other speakers highlighted how far many companies still have to travel. Around 74% of small and medium-sized enterprises use AI only at a basic level, 55% identify the skills gap as their leading barrier, and just 1% have reached genuine AI maturity. The bottleneck is therefore not simply access to tools, but talent, measurement and governance.
The conference also distinguished digital transformation from AI transformation. Digital transformation modernises existing processes, while AI transformation creates new capabilities in forecasting, automation and decision support. Companies do not need to wait for every digital programme to finish before starting with AI; both journeys can develop in parallel as their data foundations improve.
Imperfect data should not stop a well-designed pilot
Experts noted that fragmented or limited data does not necessarily prevent an organisation from testing an AI use case. Carefully designed synthetic data can help teams reach a pilot sooner, provided it reflects the patterns and characteristics of the original information rather than merely duplicating records.
Real operational data is still essential before deployment. A pilot should help the organisation identify what information is missing, how it must be collected, where it is stored and who is allowed to use it. As AI reaches deeper into day-to-day operations, data governance becomes part of competitive advantage alongside technical capability.
For A1P, the central lesson is clear: enterprise AI creates value when it is reliable, measurable and integrated into real workflows — not when it remains an impressive standalone demonstration.
Source and translation note
This article is an English adaptation of reporting published by Tuổi Trẻ Online. Images are reproduced from the original report with attribution.
Read the original Vietnamese report ↗