Enterprise AI Fluency for Tech Teams: Building a Consistent AI Capability Baseline Across Every Technical Role
About This Course
This 2-day course establishes a consistent AI capability baseline across a technology organisation, with examples drawn from banking and insurance. Day 1 explains how AI works — the differences between AI, machine learning, deep learning and generative AI, how large language models process tokens through transformer and attention mechanisms, why training and inference drive cost and latency, and why AI systems fail through hallucination, context-window limits and reasoning gaps. Participants then compare the four enterprise solution patterns (prompting, RAG, fine-tuning and agentic AI) and trace AI’s impact across seven IT domains: infrastructure and cloud, data management, cybersecurity, software development, operations, networking and project management.
Day 2 turns to evaluating, governing and driving AI adoption: building evaluation rubrics and test sets, human-in-the-loop review, production monitoring and drift detection; Singapore’s Model AI Governance Framework, PDPA and MAS FEAT, bias, explainability, shadow AI and vendor risk; and organisational AI readiness, business-case building and scaling strategies. Every module includes a hands-on lab, workshop, case study or capstone — including comparing models on the same prompt, querying a RAG knowledge base in AWS Bedrock and designing a full AI solution architecture in teams — using synthetic data only.
What You'll Learn
• Compare prompting, retrieval-augmented generation (RAG), fine-tuning and agentic AI patterns, and select the appropriate pattern for an enterprise use case. (LO2)
• Describe how AI changes day-to-day work across the seven IT domains: infrastructure, data, security, software development, operations, networking and project management. (LO3)
• Explain why AI evaluation differs from traditional software testing, and outline an evaluation rubric, test set, human-in-the-loop review and production monitoring plan. (LO4)
• Apply Singapore’s AI governance requirements — the Model AI Governance Framework, PDPA and MAS FEAT — together with bias, explainability, privacy and vendor-risk controls, to an AI deployment. (LO5)
• Assess organisational AI readiness across the five maturity dimensions and build an AI business case in business language for non-technical leaders. (LO6)
• Compare the outputs of the same prompt across different AI models and explain the causes of the differences observed. (LO7)
• Query a RAG-based knowledge base in AWS Bedrock and distinguish grounded from ungrounded answers. (LO8)
Entry Requirements
1. Read, write, and speak English at WPL Level 4
2. A working background in a technology role (infrastructure, data, security, development, operations, networking or project management)
3. Basic familiarity with enterprise IT systems and cloud concepts