Many organizations begin by asking, “Which AI model should we choose?”
However, according to Khun Joob Damrongsak Reetanon – Chief Infrastructure and Integration Officer, this question is not the most important when it comes to using AI effectively for business.
“Before asking which AI model to use, the more important question is: what business problem does the organization want the AI application to solve?”
AI Model vs AI Application
- AI Model : The “brain” or intelligent mechanism that processes data.
- AI Application : The “tool” that applies the AI’s capabilities to address real business needs.
Why Start with the Business Problem?
Investing in AI purely from a technology perspective can lead to high costs without delivering real results. On the other hand, starting from a business problem ensures that AI applications have clear objectives, such as:
- Increasing operational efficiency while reducing time and costs
- Enhancing customer experience
- Strengthening cybersecurity and data governance
- Enabling competitive new business models
Recommended Checklist Before Investing in AI
Getting the most value from AI doesn’t depend solely on choosing the “latest” technology. It starts with reviewing business problems and ensuring the supporting infrastructure is ready. Consider these points:
☑️ Define Clear Business Goals
Ensure AI applications address real business challenges.
☑️Choose the Right Tools and AI Models
Each AI model has different strengths. It’s not always necessary to use just one. Sometimes, multiple simpler models working together can achieve the desired results. In other cases, a custom AI model may need to be developed for your specific needs.
☑️Data is Critical Data quality is the foundation of reliable AI. High-quality data includes:
- Accuracy: True and correct information
- Completeness: No missing critical data
- Representativeness: Reflects the overall real-world scenario without bias
- Consistency: Uniform across all datasets
- Relevance: Directly addresses the problem at hand These factors ensure precise and trustworthy AI outputs.
☑️ Manage Risks
Cover cost optimization, data security, and governance to prevent leaks or misuse.
☑️ Build Flexible and Robust Infrastructure
Servers, storage, networks, cloud platforms, and cybersecurity form the backbone for safe and efficient AI operations.
☑️ Choose a Technology Consultant Who Understands Both Business and Infrastructure
A good consultant not only knows AI but also helps link business objectives with technology foundations effectively.
By following these steps, organizations can ensure AI investments are valuable and deliver the highest possible returns.
#AIforBusiness #CostOptimization #MFEC #AIApplication #AILifeCycle
