Course Outline
AI Fundamentals: Concepts, Types, and Common Misconceptions
- Understanding what artificial intelligence is and is not
- Distinguishing between narrow AI and general AI
- Overview of machine learning, deep learning, and data science
- A non-technical explanation of how machine learning functions
Generative AI and AI Agents in a Business Context
- The capabilities and inherent limitations of generative AI
- The mechanics and operation of AI agents
- Typical business applications for generative AI
- Understanding hallucinations and the boundaries of current tools
Data Readiness: The Foundation for AI
- Differences between structured and unstructured data
- Data quality and its essential dimensions
- Key data governance principles for managers
- The rationale for establishing data readiness before deploying AI
Generating Business Value with AI
- The AI opportunity matrix framework
- Value chain analysis for identifying AI use cases
- Evaluating primary and supporting activities
- Identifying processes that yield the highest value
AI Success Stories and Key Takeaways
- Real-world AI applications spanning various business functions
- Factors that drive successful AI implementations
- Common failure patterns and strategies to prevent them
Workshop: Identifying 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
- Collaboratively sharing and discussing cross-departmental findings
Prioritizing AI Use Cases for Maximum Impact
- Scoring methods for value versus feasibility
- Balancing quick wins with strategic long-term bets
- The AI project funnel approach
- Selecting the initial use cases for execution
AI Governance: Roles, Committees, and Accountability
- Determining leadership structures for AI within the organization
- Defining governance roles, committees, and specific responsibilities
- Comparing Center of Excellence models with distributed ownership
- Best practices for establishing robust AI governance
Security, Risk, and Responsible AI
- Navigating information security and data protection constraints
- Conducting risk assessments for AI initiatives
- Implementing ethical guidelines and responsible AI practices
- Strategies for building trustworthy AI systems
Building an AI-Ready Organization
- Evaluating the organization’s AI maturity level
- Identifying required skills and competencies for the AI journey
- Managing change and assessing cultural readiness
- The continuous AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Synthesizing the AI opportunity map
- Defining implementation phases, quick wins, and key milestones
- Assigning ownership, metrics, and governance checkpoints
- Finalizing the initial roadmap and outlining immediate next steps
Requirements
- There is no requirement for prior technical or programming expertise.
- Participants should have an interest in applying AI within business or management contexts.
Target Audience
- Senior managers and department heads.
- General managers and executives.
- Leaders overseeing digitalization and transformation 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.
