Course Outline
AI Fundamentals: Core Concepts, Classifications, and Common Misunderstandings
- Clarifying the definition and scope of artificial intelligence
- Distinguishing between narrow AI and general AI applications
- Exploring the relationships among machine learning, deep learning, and data science
- Explaining machine learning mechanisms in accessible, non-technical terms
Generative AI and AI Agents in Organizational Operations
- Evaluating the capabilities and inherent limitations of generative AI
- Understanding the operational mechanics of AI agents
- Reviewing standard operational applications of generative AI for government
- Addressing hallucination risks and the boundaries of current AI tools
Data Readiness: The Essential Foundation for AI
- Managing structured and unstructured data assets
- Assessing data quality through key operational dimensions
- Key data governance practices for administrative oversight
- Establishing data readiness as a prerequisite for AI adoption
Where AI Creates Operational Value
- Applying the AI opportunity matrix to public sector contexts
- Conducting value chain analysis for AI integration
- Evaluating primary and supporting operational activities
- Identifying processes that yield the highest strategic value
AI Success Cases and Institutional Lessons Learned
- Examining real-world AI implementations across public service functions
- Analyzing factors that contributed to successful adoption
- Identifying common failure patterns and mitigation strategies
Workshop: Identifying AI Opportunities by Departmental Unit
- Mapping departmental workflows and identifying operational bottlenecks
- Developing AI use case proposals for specific business areas
- Completing an AI opportunity assessment canvas
- Facilitating cross-departmental review and discussion of findings
Prioritizing AI Use Cases for Maximum Strategic Value
- Scoring initiatives based on value and feasibility metrics
- Balancing immediate quick wins against long-term strategic investments
- Utilizing the AI project selection funnel
- Selecting initial use cases for pilot implementation
AI Governance: Roles, Committees, and Accountability
- Defining leadership structures for AI oversight
- Establishing governance roles, committee structures, and responsibilities
- Comparing Center of Excellence models versus distributed ownership
- Adopting best practices for AI governance in the public sector
Security, Risk Management, and Responsible AI
- Addressing information security and data protection requirements
- Conducting risk assessments for AI initiatives
- Implementing ethical guidelines and responsible AI standards
- Ensuring the development of trustworthy AI systems
Building an AI-Ready Organization
- Assessing current AI maturity levels
- Identifying necessary skills and competencies for the AI transition
- Managing change and assessing organizational cultural readiness
- Implementing the iterative AI strategy cycle
Workshop: Creating the AI Implementation Roadmap and Action Plan
- Synthesizing the consolidated opportunity map
- Defining implementation phases, immediate targets, and key milestones
- Assigning accountability, performance metrics, and governance checkpoints
- Finalizing the initial roadmap and defining next steps
Requirements
- No prior technical or programming experience is required.
- A professional interest in applying AI within an administrative or management context.
Intended Audience
- Senior managers and department heads.
- General managers and executive leadership.
- Officials responsible for digitalization and organizational transformation initiatives.
Testimonials (2)
correct way of prompting and including guardrails in instructions.
YEO SHI MIN - ST Engineering Aerospace Ltd
Course - ChatGPT and Microsoft 365 Copilot for Advanced Productivity
Understand AI function n tools to make our job easier. Need to improved AI Chubb such as make analysis n creating presentation
