Course Outline
AI Fundamentals: Key Concepts, Varieties and Common Myths
- Defining what artificial intelligence actually is and what it is not
- Distinguishing between narrow AI and general AI
- Understanding machine learning, deep learning, and data science
- Explaining how machine learning functions without complex technical terminology
Generative AI and AI Agents in a Business Context
- Capabilities and constraints of generative AI
- The mechanics and function of AI agents
- Typical business applications of generative AI
- Understanding hallucinations and the current limitations of AI tools
Data Readiness: The Basis for AI Success
- Differentiating structured and unstructured data
- Key dimensions of data quality
- Essential data governance principles for managers
- The importance of establishing data readiness before deploying AI
Generating Business Value with AI
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for potential AI use cases
- Focusing on primary and supporting activities
- Identifying processes that yield the highest value
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 to prevent them
Workshop: Pinpointing AI Opportunities by Department
- Mapping departmental processes and identifying pain points
- Brainstorming AI use case ideas for each business area
- Filling out 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
- Applying the AI project 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 duties
- Comparing centers of excellence with distributed ownership models
- Best practices for effective AI governance
Security, Risk and Responsible AI
- Information security and data protection limitations
- Conducting risk assessments for AI projects
- Adhering to ethical guidelines and responsible AI usage
- Cultivating trust in AI systems
Cultivating an AI-Ready Organization
- Evaluating organizational AI maturity
- Developing necessary skills and competencies for the AI journey
- Managing change and ensuring cultural readiness
- Implementing the AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Synthesizing the opportunity map
- Establishing phases, quick wins, and key milestones
- Assigning owners, defining metrics, and setting governance checkpoints
- Finalizing the initial roadmap and subsequent steps
Requirements
- No previous technical background or coding experience is necessary.
- A genuine interest in leveraging AI within a business or managerial setting.
Target Audience
- Senior managers and department heads.
- General managers and executive-level leaders.
- 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.
