Course Outline
AI Fundamentals: Key Concepts, Variations, and Common Myths
- Distinguishing what artificial intelligence is versus what it is not
- Contrasting Narrow AI with General AI
- Overview of machine learning, deep learning, and data science
- Understanding machine learning principles without relying on technical jargon
Generative AI and AI Agents in the Business Context
- Examining the capabilities and inherent constraints of generative AI
- Understanding the mechanics and function of AI agents
- Identifying frequent business applications of generative AI
- Addressing hallucinations and recognizing the boundaries of current tools
Data Readiness: The Critical Foundation for AI
- Differentiating between structured and unstructured data
- Evaluating data quality and its core dimensions
- Essentials of data governance from a managerial perspective
- The importance of establishing data readiness prior to AI deployment
Unlocking Business Value with AI
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for AI-driven use cases
- Identifying primary and supporting business activities
- Focusing on processes that yield the highest value
AI Success Stories and Key Lessons Learned
- Exploring real-world AI applications across various business functions
- Understanding the factors that drive successful implementations
- Identifying common failure patterns and strategies to prevent them
Workshop: Mapping AI Opportunities by Department
- Charting departmental processes and identifying pain points
- Brainstorming AI use case ideas specific to each business area
- Completing an AI opportunity canvas
- Reviewing and discussing findings across different departments
Prioritizing AI Use Cases for Optimal Value
- Scoring based on value versus feasibility
- Balancing quick wins with long-term strategic investments
- Navigating the AI project funnel
- Selecting the initial use cases to pursue
AI Governance: Establishing Roles, Committees, and Accountability
- Determining the appropriate leadership structure for AI within the organization
- Defining governance roles, committee structures, and responsibilities
- Comparing a Center of Excellence model with distributed ownership
- Implementing best practices for effective AI governance
Security, Risk Management, and Responsible AI
- Navigating information security and data protection requirements
- Conducting risk assessments for AI initiatives
- Applying ethical guidelines and responsible AI usage standards
- Building trust through reliable AI practices
Cultivating an AI-Ready Organization
- Evaluating the organization's current AI maturity level
- Developing the necessary skills and competencies for the AI journey
- Managing change and ensuring cultural readiness
- Understanding the continuous cycle of AI strategy
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Synthesizing the identified opportunities into a cohesive map
- Defining implementation phases, quick wins, and key milestones
- Assigning owners, establishing metrics, and setting governance checkpoints
- Finalizing the initial roadmap and outlining immediate next steps
Requirements
- No previous technical background or programming experience is necessary.
- A professional interest in leveraging AI within a business or leadership context.
Target Audience
- Senior managers and department heads.
- General managers and C-suite executives.
- Leaders overseeing digital transformation and modernization 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.
