Course Outline
AI Fundamentals: Core Concepts, Classifications, and Common Myths
- Defining the scope and limitations of artificial intelligence
- Distinguishing 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 Enterprise
- Capabilities and constraints of generative AI
- The functionality and mechanics of AI agents
- Standard business applications of generative AI
- Understanding hallucinations and the boundaries of current tools
Data Readiness: The Cornerstone of AI
- Differentiating between structured and unstructured data
- Key dimensions of data quality
- Essentials of data governance for management
- The critical importance of data readiness prior to AI implementation
Generating Business Value with AI
- The AI opportunity matrix
- Value chain analysis for identifying AI use cases
- Assessing primary and supporting activities
- Identifying high-value business processes
AI Success Stories and Key Takeaways
- Real-world AI applications across various business functions
- Factors contributing to successful implementations
- Recognizing common failure patterns and mitigation strategies
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
- Cross-departmental review and discussion of findings
Strategic Prioritization of AI Use Cases
- Scoring based on value versus feasibility
- Balancing quick wins with strategic long-term investments
- The AI project selection funnel
- Selecting the initial use cases for execution
AI Governance: Leadership, Committees, and Accountability
- Determining leadership structures for AI in the organization
- Defining governance roles, committees, and responsibilities
- Comparing Center of Excellence models with distributed ownership
- Best practices for effective AI governance
Security, Risk Management, and Responsible AI
- Information security and data protection requirements
- Conducting risk assessments for AI initiatives
- Adhering to ethical guidelines and responsible AI practices
- Establishing trustworthy AI systems
Cultivating an AI-Ready Organization
- Evaluating organizational AI maturity
- Developing skills and competencies for the AI journey
- Change management and assessing cultural readiness
- The continuous AI strategy cycle
Workshop: Developing the AI Deployment Roadmap and Action Plan
- Synthesizing the opportunity map
- Defining phases, quick wins, and key milestones
- Assigning ownership, metrics, and governance checkpoints
- Finalizing the initial roadmap and immediate next steps
Requirements
- No background in technology or programming is necessary.
- A professional interest in integrating AI into business and management workflows.
Target Audience
- Senior managers and department heads.
- General managers and executive leadership.
- 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.
