JUNE 20259 AI is not just applying advanced analysis and logic- based techniques, like Excel, machine learning (ML), to support decision making. But AI also needs to automatically perform intelligent actions for humansday operations; use vast amount of data so that we can identify cause and effect (causality) of interested outcomes.Table 1 shows the framework which you need to consider and pay attention to, especially the bottom `Enabler Layer' as we need resources to deliver. AI is a multi-discipline team that needs knowledge in Mathematics, Statistics, Calculus, Algorithm, Coding, and communication. As you are building the AI/ML team- you need to be excellent in the data domain and process, but you need to develop the data culture around such as working with stakeholders to show how AI can help bring the business values.Risks and drawbacks in adopting AI: expectations where there is an expectation AI can be implemented quickly. Sometime we may not be lucky to get the right model and accuracy. We need to make our expectations clear; data is not sufficient or quality of data is not met; executive and users are not supporting AI projects, as some users may resist or afraid of losing control/job; AI Security, fairness and ethics, these to be discussed at the early stages; corporate culture, such as meetings, presentations, trainings and administrative works which deprive AI engineers' time to model, perform mathematical analysis, and code hundreds of computers in order to achieve business benefits. I hope with the trend of AI, we can expect organizations to adopt AI successfully and improve business benefits.
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