Course Outline
AI Fundamentals: Key Concepts, Categories and Common Misunderstandings
- Defining what artificial intelligence is and is not
- Comparing Narrow AI with General AI
- Understanding machine learning, deep learning, and data science
- Explaining machine learning mechanisms without complex technical jargon
Generative AI and AI Agents in a Business Context
- The capabilities and constraints of generative AI
- How AI agents function and operate
- Typical business applications of generative AI
- Understanding hallucinations and the boundaries of current tools
Data Readiness: The Bedrock of AI
- Distinguishing between structured and unstructured data
- Data quality and its critical dimensions
- Essential data governance principles for managers
- The importance of establishing data readiness before implementing AI
Identifying Areas Where AI Delivers Business Value
- The AI opportunity matrix
- Value chain analysis for identifying AI use cases
- Primary versus supporting business activities
- Processes that offer the highest potential for value creation
AI Success Stories and Key Takeaways
- Real-world AI applications across various business functions
- Factors that drive successful AI implementations
- Common failure patterns and strategies to prevent them
Workshop: Mapping 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 insights across different departments
Prioritizing AI Use Cases for Optimal Impact
- Scoring value against feasibility
- Quick wins compared to long-term strategic investments
- The AI project selection funnel
- Choosing the initial use cases to pursue
AI Governance: Roles, Committees and Accountability
- Determining who should lead AI initiatives within the organization
- Defining governance roles, committees, and responsibilities
- Center of Excellence models versus distributed ownership
- Best practices for effective AI governance
Security, Risk and Responsible AI
- Information security and data protection considerations
- Risk assessment methodologies for AI initiatives
- Ethical standards and responsible use of AI
- Cultivating trust in AI systems
Building an Organization Ready for AI
- Evaluating current AI maturity levels
- Essential skills and competencies for the AI journey
- Change management and preparing the organizational culture
- The continuous AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating the identified opportunity map
- Setting phases, quick wins, and key milestones
- Assigning ownership, defining metrics, and establishing governance checkpoints
- Finalizing the initial roadmap and outlining next steps
Requirements
- No previous technical background or programming experience is necessary.
- A genuine interest in applying AI within a business or management environment.
Target Audience
- Senior managers and department heads.
- General managers and executive leaders.
- Leadership teams 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.
