Course Outline
AI Fundamentals: Core Concepts, Varieties, and Common Myths
- Defining the true scope of artificial intelligence and what it excludes
- Distinguishing between Narrow AI and General AI
- Understanding the relationship between machine learning, deep learning, and data science
- Explaining machine learning mechanisms without relying on technical jargon
Generative AI and AI Agents in the Business Context
- The capabilities and inherent limitations of generative AI
- How AI agents function and operate
- Typical business applications for generative AI
- Addressing hallucinations and the current boundaries of AI tools
Data Readiness: The Essential Foundation for AI
- Differentiating between structured and unstructured data
- Key dimensions of data quality
- Essential data governance principles for managers
- The importance of establishing data readiness before deploying AI
Identifying Where AI Drives Business Value
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for AI-specific use cases
- Examining primary and supporting business activities
- Highlighting processes that offer the greatest potential for value creation
AI Success Stories and Key Lessons
- Real-world examples of AI application across various business functions
- Factors contributing to successful AI implementations
- Common failure patterns and strategies to avoid them
Workshop: Discovering AI Opportunities by Department
- Mapping departmental processes and identifying pain points
- Brainstorming AI use case ideas for each business area
- Completing an AI opportunity canvas
- Sharing and debating findings across different departments
Prioritizing AI Use Cases for Optimal Value
- Scoring projects based on value versus feasibility
- Balancing quick wins against long-term strategic investments
- Navigating the AI project funnel
- Selecting the initial use cases to pursue
AI Governance: Leadership, Committees, and Accountability
- Determining the appropriate leadership structure for AI in the organization
- Defining governance roles, committees, and specific responsibilities
- Choosing between a Center of Excellence model and distributed ownership
- Implementing best practices for AI governance
Security, Risk Management, and Responsible AI
- Navigating information security and data protection constraints
- Conducting risk assessments for AI initiatives
- Adhering to ethical guidelines and responsible AI usage
- Building trustworthy AI systems
Cultivating an AI-Ready Organization
- Evaluating current AI maturity levels
- Developing the necessary skills and competencies for the AI journey
- Managing change and preparing the organizational culture
- Understanding the AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Synthesizing the opportunity map
- Defining phases, quick wins, and key milestones
- Assigning owners, performance metrics, and governance checkpoints
- Finalizing the initial roadmap and outlining next steps
Requirements
- No previous technical or programming experience is necessary.
- A genuine interest in applying AI within a business or management setting.
Target Audience
- Senior managers 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.
