1. Lack of quality data

Data is the backbone of any AI system. However, many businesses lack a consistent data collection and storage system, leading to data shortages or inaccuracies. This negatively affects the accuracy and performance of AI models.

Solution: Establish a professional data governance process, use data cleansing and standardization tools, and invest in automated data collection systems.

2. Inadequate technology infrastructure

Many small and medium-sized businesses in Vietnam do not yet have proper servers, cloud systems, or big data tools to support AI implementation.

Solution: Prioritize using AI-as-a-Service (AIaaS) platforms and leverage cloud services such as AWS, Google Cloud, and Azure to reduce upfront investment costs.

3. Shortage of AI experts and technical staff

The talent pool for AI in Vietnam is still limited. Many companies face difficulties in recruiting or training internal teams to operate AI systems.

Solution: Partner with AI solution providers, outsource AI development, or build an internal training roadmap in collaboration with academic institutions.

4. High initial investment costs

AI requires investment in software, hardware, skilled personnel, and system maintenance, causing many businesses to worry about return on investment.

Solution: Start with small AI projects (pilots), calculate ROI clearly, and choose use cases with the potential for tangible financial benefits.

5. Difficulty integrating with existing processes

AI is not always easy to integrate with existing ERP, CRM systems, or business workflows.

Solution: Choose AI solutions with flexible APIs and collaborate with system integration experts to ensure technical compatibility and optimal performance.

6. Security and AI ethics concerns

AI usage can lead to risks such as data breaches, biased decisions in automated processes, or unethical usage.

Solution: Adhere to AI ethical standards, establish clear data privacy policies, and regularly audit models to detect and correct algorithmic bias.

7. Lack of clear strategy

Some businesses apply AI reactively or follow trends without defining clear goals, roadmaps, or KPIs—leading to failure or wasted resources.

Solution: Develop an AI strategy aligned with the overall business strategy, define short- and long-term goals, and use measurable indicators for evaluation.

8. Overcoming challenges: A sustainable AI implementation roadmap

To overcome the above challenges, businesses should follow a structured AI deployment roadmap:

• Phase 1: Assess current capabilities and define business objectives.
• Phase 2: Select high-priority use cases and run pilot projects.
• Phase 3: Scale and optimize AI models.
• Phase 4: Integrate AI solutions and train internal teams for long-term operation.

Additionally, staying up to date with technology trends and working with experts are keys to minimizing risks in the digital transformation journey through AI.