8 | |DECEMBER 2025IN MY OPINIONBy Kreecha Puphaiboon, Head of AI/ML,BANPU Public Company Limited[BKK: BANPU]ENTERPRISE AI ADOPTION BASED ON EXPERIENCEPersonally, the most important step is to set the definition of Artificial Intelligence (AI) in your organization. Peter Norvig from Google defines AI as the designing and building of intelligent agents that perceives from the environment and take actions that affect that environment. 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 humans. The definition of AI must be clearly agreed from all levels within the organization e.g., boards, management and operation. Otherwise, we will see confusion. For examples, I was asked to use Excel to perform loans and asset valuation with thousands of customers in real-time and users still want to edit and verify. Then, people showed me that they can do AI with Excel decision tables, solver, or linear regression. To me this is data analytics where people still want to analyze data, make judgements or conclusions about the information.Generally, AI is expected to improve businesses. Thus, business benefits or AI objectives must be clearly quantified so that AI results can be measured. Consequently, all stakeholders can have the same business target. My past AI project goals were: reducing operational costs ­how much in dollar (explicitly 1 million USD/year), improving customer experience (click-through-rate increased by 3 percent after 2 months) and increasing revenue by 10 percent of loans and deposits. These clear numerical objectives helped in implementing AI projects and tracking outcomes on annual basis.There are business opportunities where AI can improve Banpu abilities to: increase productivity and operational efficiencies; save time and money by automating and optimizing our coal mining routine tasks; make faster business decisions by adopting AI techniques; minimize human errors on day-to-day operations; use vast amount of data so that we can identify cause and effect (causality) of interested outcomes.For AI Adoption in the organization, we must have Enterprise AI Strategy: centralize, decentralize or hybrid where data and models are managed by whom; educate management about data, data strategy/governance, data is vital as AI needs data to process. If data is wrong, then AI will act badly. Data governance is crucial so when having issues with data we need someone to escalate, permit, fix and deploy; infrastructure whether on-premises, cloud or hybrid because you will need to enable skills of engineers. 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.Table 1: Enterprise AI Adoption FrameworkA couple of points to note: have a clearly defined and measurable objective that creates business value e.g., financial benefits; If you have multiple ideas for the business ­ go for low hanging fruits first and measure business values, Business functions · Marketing · Customer Relationship Management · Sales · Service · Market Research · Energy Trading · Operation Al Use Cases · Sales Prediction · Pricing · Lead Prediction · Automated Customer Service · Process Automation · Fraud Detection · Media Planning · Content Creation · Chatbots · Product Recommendation Al- Methods · Robotics · Supervised Learning · Rule Based Systems · Unsupervised Learning · Machine Learning · Computer Vision · Reinforcement Learning · Neural Network · Optimization Data Layer · IoT · Mobile · Sensors · Volume · Velocity · Variety · Digital: numbers, texts, images and videos Enabler Layer · Internet Technologies · Multicore Processor · Distributed Computing · Cloud computing · Graphics Processing Unit (GPU) · Machine Learning Operation (MLOps)
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