AI GUIDE FOR BOARDS IN SINGAPORE Section I: The Board Mandate
The AI Lexicon
To lead eectively in the age of AI, boards must strengthen their AI literacy. At a minimum, directors should understand how AI is reshaping industries, operating models and competitive dynamics. e terminology continues to evolve rapidly, and many de nitions overlap in practice. e descriptions below are intentionally simpli ed for the purpose of this Guide, and reect the landscape as of June 2026. • Arti cial intelligence: A broad term to describe systems capable of performing tasks typically associated
complete tasks on behalf of users, o en across multiple steps and systems. Unlike traditional AI tools which typically refer to rules-based algorithms, agentic AI can pursue goals, use external tools, make decisions within de ned parameters and manage end-to-end workows, beyond isolated tasks. • Predictive AI: AI focused primarily on forecasting outcomes based on historical data. Unlike generative AI, predictive AI does not create new content, but instead supports areas such as demand forecasting, risk assessment and operational planning. • Multimodal AI: AI systems capable of processing and integrating multiple forms of data simultaneously, including text, images, audio, video and structured data. • Responsible AI: e governance principles, controls and practices designed to ensure AI systems are safe, ethical, transparent, fair and aligned with legal and societal expectations. Key concerns include safety, bias, explainability, privacy, cyber security and accountability. • AI Assistant: AI-powered tools embedded within workplace so ware that assist users with tasks such as writing, research, analysis, coding and workow automation. ese systems are designed to augment human productivity rather than fully automate roles. • Autonomous system: AI-enabled systems capable of operating with minimal human intervention. Examples include autonomous vehicles, robotics, automated trading systems and self- managed operational platforms.
with human intelligence, such as understanding natural language,
recognising patterns, reasoning from data, and supporting decision-making. • Machine Learning: A subset of AI where systems learn patterns from datasets to make predictions or generate • Large Language Model (LLM): An advanced AI model trained on large amounts of text data that can understand and generate human-like language. LLMs underpin many generative AI tools and applications, including chatbots, copilots, search assistants and automated content generation. • Generative AI: AI systems that can outputs without being explicitly programmed for every scenario. generate new content, such as text, images, audio, video or so ware code. Its business value lies in augmenting “knowledge-intensive” work, including summarisation, dra ing, research, analysis, coding and complex data synthesis. • Agentic AI: AI systems that can independently plan, execute and
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