Course Outline
AI Fundamentals: Key Concepts, Categories, and Common Myths
- Clarifying the true scope and limits of artificial intelligence.
- Distinguishing between Narrow AI and General AI.
- Exploring machine learning, deep learning, and data science.
- Understanding machine learning mechanisms without technical jargon.
Generative AI and AI Agents in the Business Context
- Assessing the capabilities and constraints of generative AI.
- Understanding the mechanics and purpose of AI agents.
- Reviewing standard business applications for generative AI.
- Addressing hallucinations and the current boundaries of AI tools.
Data Readiness: The Bedrock of AI Success
- Differentiating between structured and unstructured data.
- Evaluating data quality and its critical dimensions.
- Understanding essential data governance principles for managers.
- Why establishing data readiness is a prerequisite for AI adoption.
Identifying Where AI Generates Business Value
- Utilizing the AI opportunity matrix.
- Conducting value chain analysis for specific AI use cases.
- Mapping primary and support activities.
- Identifying processes that yield the highest value.
AI Success Stories and Key Takeaways
- Examining real-world AI implementations across various business functions.
- Analyzing the factors behind successful AI adoptions.
- Recognizing common pitfalls 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.
- Collaborating to share and discuss cross-departmental findings.
Prioritizing AI Use Cases for Optimal Impact
- Scoring projects based on value versus feasibility.
- Balancing quick wins with long-term strategic bets.
- Applying the AI project funnel model.
- Selecting the initial use cases to execute.
AI Governance: Defining Roles, Committees, and Accountability
- Determining the appropriate leadership structure for AI.
- Defining governance roles, committees, and specific responsibilities.
- Choosing between a Center of Excellence and distributed ownership models.
- 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.
- Adhering to ethical guidelines and responsible AI principles.
- Fostering the development of trustworthy AI systems.
Cultivating an AI-Ready Organization
- Evaluating the organization’s current AI maturity.
- Identifying required skills and competencies for the AI journey.
- Managing change and preparing the organizational culture.
- Executing the AI strategy cycle effectively.
Workshop: Formulating the AI Implementation Roadmap and Action Plan
- Synthesizing the AI opportunity map.
- Defining implementation phases, quick wins, and key milestones.
- Assigning ownership, defining metrics, and setting governance checkpoints.
- Finalizing the initial roadmap and determining next steps.
Requirements
- No prior technical or programming background is necessary.
- A keen interest in applying AI within business and management contexts.
Target Audience
- Senior managers and department heads.
- General managers and C-suite executives.
- Leaders overseeing digital transformation and innovation 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.
