GA100: Generative AI for Automation and Productivity (Synchronous E- Learning)
About This Course
- Many professionals associate AI capability primarily with prompt engineering or basic use of tools such as ChatGPT. These are introduced as foundational skills, but represent only an entry point to effective AI adoption.
- This course emphasises applied AI, focusing on AI agents and workflow-based automation to drive measurable productivity and operational improvements.
- Designed for C-suite leaders, managers, and professionals responsible for advancing AI adoption within organisations, as well as individual contributors seeking practical, high-impact AI skills.
- Content is grounded in real enterprise use cases and has been delivered to management teams and professionals from multinational organisations in Singapore.
- Upon completion, learners are expected to attain a level of applied AI competency comparable to the top 80–90 percentile of AI-enabled professionals.
What You'll Learn
- Explain the underlying mechanisms of large language models (LLMs) that power applications like ChatGPT, Claude, and Gemini, including why issues such as AI hallucinations occur and how to manage them in practice
- Use generative AI tools for real workplace tasks such as drafting emails, writing reports, generating images, and producing voice or multimedia content using tools such as NanoBanana, ElevenLabs, and Suno
- Design effective prompt engineering techniques and apply them directly within real-world professional workflows rather than standalone experimentation
- Use AI agents, with Manus AI as a practical example, to schedule tasks, build and deploy analytics dashboards, and generate outputs such as websites, slides, and PDFs, all without writing code
- Build AI-powered automation workflows using tools such as n8n to automate repetitive and time-consuming tasks across different business functions without coding
- Apply Retrieval-Augmented Generation (RAG) concepts to enable AI systems to work with internal documents, knowledge bases, and proprietary data securely
- Use Model Context Protocol (MCP) concepts, including MCP tools and conversational workflows, to integrate and scale AI solutions effectively
- Understand AI governance, ethical considerations, and organisational guardrails, and apply a practical policy playbook to ensure responsible, compliant, and controlled use of AI within organisations
Entry Requirements
While no prior technical or coding experience is required, learners must possess:
1. English Language Proficiency:
- Ability to communicate clearly in spoken and written English
2. Digital Literacy:
- Familiarity with basic digital tools, such as email and web browsing