Course Outline
AI Fundamentals: Key Concepts, Variations, and Common Misconceptions
- Defining what artificial intelligence actually is and what it is not
- Distinguishing between narrow AI and general AI
- Understanding the relationship between machine learning, deep learning, and data science
- Explaining machine learning mechanisms without technical jargon
Generative AI and AI Agents in a Business Context
- The capabilities and inherent limitations of generative AI
- The mechanics of AI agents
- Typical business applications for generative AI
- Understanding hallucinations and the boundaries of current tools
Data Readiness: The Essential Foundation for AI
- Differences between structured and unstructured data
- Data quality metrics and their key dimensions
- Core data governance principles for managers
- The rationale for prioritizing data readiness before AI deployment
Identifying Business Value Through AI
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for AI use cases
- Evaluating primary and supporting activities
- Recognizing processes that yield the highest value
AI Success Stories and Key Takeaways
- Real-world AI applications across various business functions
- Factors behind successful AI implementations
- Common failure patterns and strategies to mitigate them
Workshop: Pinpointing 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 debating findings across different departments
Prioritizing AI Use Cases for Optimal Value
- Scoring based on value versus feasibility
- Balancing quick wins with long-term strategic bets
- Implementing the AI project funnel
- Selecting the initial use cases for development
AI Governance: Roles, Committees, and Accountability
- Identifying the right leaders for AI in the organization
- Defining governance roles, committees, and duties
- Choosing between a Center of Excellence and distributed ownership models
- Adopting best practices for AI governance
Security, Risk Management, and Responsible AI
- Navigating information security and data protection requirements
- Conducting risk assessments for AI projects
- Applying ethical guidelines and responsible AI practices
- Building trust in AI systems
Cultivating an AI-Ready Organization
- Evaluating current AI maturity levels
- Developing the skills and competencies needed for the AI journey
- Managing change and ensuring cultural readiness
- Executing the AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating the AI opportunity map
- Structuring phases, quick wins, and key milestones
- Assigning ownership, metrics, and governance checkpoints
- Finalizing the initial roadmap and defining immediate next steps
Requirements
- No prior technical background or programming experience is necessary.
- A genuine interest in applying AI within business and management frameworks.
Target Audience
- Senior management and department heads.
- General managers and C-suite executives.
- Leaders overseeing digitalization and transformation projects.
Testimonials (2)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
