Course Outline
AI Fundamentals: Key Concepts, Varieties, and Common Myths
- Clarifying what artificial intelligence is and what it is not
- Distinguishing between Narrow AI and General AI
- Understanding machine learning, deep learning, and data science
- Explaining how machine learning operates without technical jargon
Generative AI and AI Agents in the Business Context
- Exploring the capabilities and inherent limitations of generative AI
- Understanding AI agents and their operational mechanisms
- Identifying common business applications of generative AI
- Addressing hallucinations and the current boundaries of AI tools
Data Readiness: The Essential Foundation for AI
- Differentiating between structured and unstructured data
- Evaluating data quality and its critical dimensions
- Key data governance principles for managers
- The importance of data readiness preceding AI implementation
Creating Business Value with AI
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for potential AI use cases
- Examining primary and supporting business activities
- Identifying processes that yield the greatest value
AI Success Stories and Key Lessons Learned
- Reviewing real-world AI applications across various business functions
- Analyzing the factors that drive successful implementations
- Recognizing common failure patterns and strategies to prevent them
Workshop: Spotting 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 debating findings across different departments
Prioritizing AI Use Cases for Optimal Value
- Scoring based on value versus feasibility
- Balancing quick wins with strategic long-term bets
- Applying the AI project funnel model
- Selecting the initial use cases to prioritize
AI Governance: Roles, Committees, and Accountability
- Determining the appropriate leadership for AI in the organization
- Defining governance roles, committees, and accountability structures
- Comparing a Center of Excellence approach with distributed ownership
- Implementing best practices for effective 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 through transparent AI practices
Cultivating an AI-Ready Organization
- Evaluating the organization's AI maturity level
- Developing the necessary skills and competencies for the AI journey
- Managing change and ensuring cultural readiness
- Understanding the continuous AI strategy cycle
Workshop: Formulating the AI Implementation Roadmap and Action Plan
- Consolidating the AI opportunity map
- Defining implementation phases, quick wins, and key milestones
- Assigning owners, success metrics, and governance checkpoints
- Finalizing the initial roadmap and outlining next steps
Requirements
- No previous technical or programming experience is required.
- A keen interest in leveraging AI within a business or management setting.
Target Audience
- Senior managers and department heads.
- General managers and C-level executives.
- Leaders overseeing digitalization and 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
