Course Outline
AI Foundations: Key Concepts, Varieties, and Common Myths
- Distinguishing between what artificial intelligence is and is not
- Differences between narrow AI and general AI
- Overview of machine learning, deep learning, and data science
- Understanding machine learning mechanisms without complex technical jargon
Generative AI and AI Agents in a Business Context
- Examining the capabilities and inherent limitations of generative AI
- Understanding AI agents and their operational mechanics
- Typical business applications of generative AI tools
- Recognizing hallucinations and the current constraints of AI tools
Data Preparation: The Bedrock of AI Strategy
- Understanding structured versus unstructured data
- Assessing data quality and its critical dimensions
- Essential data governance principles for managers
- The critical importance of data readiness prior to AI adoption
Identifying Where AI Drives Business Value
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for AI applications
- Evaluating primary and support activities
- Identifying processes with the highest value generation potential
AI Success Stories and Key Takeaways
- Reviewing real-world AI applications across different business functions
- Analyzing the factors behind successful implementations
- Identifying common failure patterns and strategies to avoid them
Practical Workshop: Spotting AI Opportunities by Department
- Mapping departmental processes and identifying pain points
- Brainstorming AI use case ideas for specific business areas
- Completing an AI opportunity canvas
- Sharing and critiquing findings across departments
Prioritizing AI Use Cases for Optimal Value
- Scoring based on value versus feasibility
- Distinguishing between quick wins and strategic long-term bets
- Navigating the AI project funnel
- Selecting the initial use cases to pursue
AI Governance: Roles, Oversight, and Accountability
- Determining who should lead AI initiatives in the organization
- Defining governance roles, committees, and specific responsibilities
- Choosing between a Center of Excellence and distributed ownership models
- Applying best practices in AI governance
Security, Risk Management, and Responsible AI
- Complying with information security and data protection regulations
- Conducting risk assessments for AI projects
- Adhering to ethical guidelines and responsible AI usage
- Developing trustworthy AI systems
Cultivating an AI-Ready Organization
- Evaluating current AI maturity levels
- Identifying required skills and competencies for the AI journey
- Managing change and ensuring cultural readiness
- Implementing the AI strategy cycle
Practical Workshop: Developing the AI Implementation Roadmap and Action Plan
- Synthesizing the opportunity map
- Defining 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 background or programming skills are necessary.
- A general interest in leveraging AI within a business or management framework.
Target Audience
- Senior managers and department heads.
- General managers and executive-level leaders.
- Leadership responsible for digitalization and transformation projects.
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
