Course Outline
AI Fundamentals: Key Concepts, Categories, and Common Misunderstandings
- Distinguishing what artificial intelligence is and is not
- Differences between narrow AI and general AI
- Overview of machine learning, deep learning, and data science
- Explaining machine learning mechanics without technical jargon
Generative AI and AI Agents in Business Contexts
- Capabilities and constraints of generative AI
- How AI agents function
- Typical business applications of generative AI
- Understanding hallucinations and current tool limitations
Data Readiness: The Essential Foundation for AI
- Distinguishing between structured and unstructured data
- Data quality and its critical dimensions
- Essentials of data governance for managers
- The necessity of data readiness prior to AI adoption
Identifying Business Value in AI
- The AI opportunity matrix
- Value chain analysis for AI use cases
- Evaluating primary and supporting activities
- Processes that offer the greatest value potential
AI Success Stories and Key Lessons
- Real-world AI applications across various business functions
- Factors contributing to successful implementations
- Common failure patterns and strategies to avoid them
Workshop: Identifying AI Opportunities by Department
- Mapping departmental processes and identifying pain points
- Developing AI use case ideas for specific business areas
- Completing an AI opportunity canvas
- Sharing and discussing insights across departments
Prioritizing AI Use Cases for Optimal Value
- Scoring based on value versus feasibility
- Distinguishing between quick wins and strategic investments
- The AI project funnel
- Selecting the initial use cases to pursue
AI Governance: Roles, Committees, and Accountability
- Determining leadership roles for AI within the organization
- Defining governance roles, committees, and responsibilities
- Choosing between a Center of Excellence and distributed ownership models
- Best practices for effective AI governance
Security, Risk, and Responsible AI
- Information security and data protection requirements
- Risk assessment strategies for AI initiatives
- Ethical guidelines and the practice of responsible AI
- Building trustworthy AI systems
Cultivating an AI-Ready Organization
- Evaluating AI maturity levels
- Skills and competencies required for the AI journey
- Change management and cultural preparedness
- The AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating the opportunity map
- Defining phases, quick wins, and key milestones
- Assigning ownership, metrics, and governance checkpoints
- Creating the initial roadmap and outlining next steps
Requirements
- No prior technical or programming background is necessary.
- A general interest in leveraging AI within a business or management framework.
Target Audience
- Senior managers and department heads.
- General managers and executives.
- Leaders overseeing digitalization and transformation initiatives.
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.
