Course Outline
AI Fundamentals: Core Concepts, Varieties, and Common Myths
- Distinguishing what AI actually does versus common misconceptions
- The difference between Narrow AI and General AI
- Overview of machine learning, deep learning, and data science
- Understanding machine learning mechanics without technical jargon
Generative AI and AI Agents in the Business Context
- Capabilities and constraints of generative AI
- The functionality and mechanics of AI agents
- Typical business applications of generative AI
- Understanding hallucinations and the boundaries of current tools
Data Readiness: The Foundation for AI
- Structured versus unstructured data types
- Key dimensions of data quality
- Essential data governance concepts for managers
- The critical importance of data readiness prior to AI adoption
Generating Business Value with AI
- The AI opportunity matrix
- Value chain analysis tailored for AI use cases
- Primary and supporting business activities
- Processes that yield the highest value returns
AI Success Stories and Key Takeaways
- Real-world AI applications across various business functions
- Factors contributing to successful AI implementations
- Common failure patterns and strategies for avoidance
Workshop: Identifying Departmental AI Opportunities
- Mapping departmental processes and identifying pain points
- Brainstorming AI use case ideas for specific business areas
- Completing an AI opportunity canvas
- Cross-departmental sharing and discussion of insights
Prioritizing AI Use Cases for Optimal Value
- Scoring based on value versus feasibility
- Balancing quick wins with strategic investments
- The AI project funnel process
- Selecting the initial use cases for execution
AI Governance: Leadership, Committees, and Accountability
- Determining who should lead AI initiatives within the organization
- Defining governance roles, committees, and duties
- Center of Excellence models versus distributed ownership
- Best practices for effective AI governance
Security, Risk Management, and Responsible AI
- Constraints related to information security and data protection
- Risk assessment methods for AI projects
- Ethical guidelines and principles of responsible AI usage
- Developing trustworthy AI systems
Creating an AI-Ready Organization
- Evaluating the current level of AI maturity
- Required skills and competencies for the AI journey
- Change management strategies and cultural readiness
- The continuous AI strategy cycle
Workshop: Drafting the AI Implementation Roadmap and Action Plan
- Synthesizing the identified opportunities
- Defining phases, quick wins, and key milestones
- Assigning owners, metrics, and governance checkpoints
- Finalizing the initial roadmap and outlining next steps
Requirements
- Technical or programming background is not necessary.
- A keen interest in leveraging AI within business and management settings.
Target Audience
- Senior managers and department heads.
- General managers and 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.
