Course Outline
AI Foundations: Concepts, Categories, and Common Myths
- Distinguishing what artificial intelligence is and is not
- Comparing narrow AI with general AI
- Exploring machine learning, deep learning, and data science
- Understanding machine learning mechanics without technical complexity
Generative AI and AI Agents in a Business Context
- Evaluating the capabilities and boundaries of generative AI
- Understanding the functioning of AI agents
- Reviewing prevalent business applications of generative AI
- Addressing hallucinations and the constraints of current AI tools
Data Readiness: The Critical Foundation for AI
- Differentiating between structured and unstructured data
- Assessing data quality and its fundamental dimensions
- Key data governance principles for managers
- The importance of establishing data readiness prior to AI deployment
Identifying Where AI Drives Business Value
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for potential AI use cases
- Focusing on primary and supporting activities
- Highlighting processes with the highest value potential
AI Success Stories and Key Takeaways
- Examining real-world AI applications across various business functions
- Analyzing the factors behind successful AI implementations
- Recognizing common failure patterns and strategies for prevention
Workshop: Identifying AI Opportunities by Department
- Mapping departmental processes and identifying pain points
- Brainstorming AI use case ideas for each business area
- Completing an AI opportunity canvas
- Sharing and critiquing findings across different departments
Prioritizing AI Use Cases for Optimal Value
- Applying value versus feasibility scoring methods
- Balancing quick wins against strategic long-term investments
- Navigating the AI project funnel
- Selecting the initial use cases for execution
AI Governance: Roles, Committees, and Accountability
- Determining leadership structures for AI within the organization
- Defining governance roles, committees, and specific responsibilities
- Choosing between a Center of Excellence and distributed ownership models
- Implementing best practices for AI governance
Security, Risk Management, and Responsible AI
- Navigating information security and data protection constraints
- Conducting risk assessments for AI initiatives
- Adhering to ethical guidelines and responsible AI usage
- Building trust in AI systems
Cultivating an AI-Ready Organization
- Evaluating current AI maturity levels
- Identifying skills and competencies required for the AI journey
- Managing change and ensuring cultural readiness
- Understanding the AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating the identified opportunity map
- Defining implementation phases, quick wins, and key milestones
- Assigning ownership, metrics, and governance checkpoints
- Formulating the initial roadmap and defining next steps
Requirements
- No background in technology or programming is necessary.
- A keen interest in leveraging AI within business and management contexts.
Target Audience
- Senior managers and department leaders.
- General managers and C-suite executives.
- Leaders overseeing digital transformation and modernization 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
