Course Outline
AI Fundamentals: Key Concepts, Categories, and Common Misunderstandings
- Distinguishing what artificial intelligence is and is not
- Comparing Narrow AI with general AI
- Overview of machine learning, deep learning, and data science
- Explaining machine learning processes in accessible language
Generative AI and AI Agents in Business Contexts
- Understanding the capabilities and boundaries of generative AI
- The mechanics and function of AI agents
- Typical business applications of generative AI
- Addressing hallucinations and the current limitations of AI tools
Data Readiness: The Bedrock of AI Success
- Differentiating between structured and unstructured data
- Key dimensions of data quality
- Essential data governance principles for managers
- The critical importance of data readiness prior to AI deployment
Maximizing Business Value with AI
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for AI applications
- Identifying primary and supporting business activities
- Recognizing processes that yield the highest value
AI Success Stories and Key Takeaways
- Real-world examples of AI across various business functions
- Factors that contribute to successful AI implementations
- Common patterns of failure and strategies to prevent them
Workshop: Spotting AI Opportunities by Department
- Mapping departmental workflows and identifying pain points
- Generating AI use case ideas for specific business areas
- Completing an AI opportunity canvas
- Discussing and sharing insights across different departments
Prioritizing AI Use Cases for Optimal Impact
- Scoring based on value versus feasibility
- Balancing quick wins with long-term strategic investments
- Applying the AI project funnel
- Selecting the initial use cases to implement
AI Governance: Leadership, Committees, and Accountability
- Determining who should lead AI initiatives within the organization
- Defining governance roles, committees, and duties
- Center of Excellence models versus distributed ownership
- Best practices for effective AI governance
Security, Risk Management, and Responsible AI
- Information security and data protection requirements
- Assessing risks associated with AI projects
- Ethical standards and responsible AI practices
- 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 preparing for cultural adaptation
- Understanding the continuous AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating the identified opportunity map
- Setting phases, quick wins, and key milestones
- Assigning ownership, metrics, and governance checkpoints
- Finalizing the initial roadmap and defining next steps
Requirements
- No previous technical background or coding experience is necessary.
- A genuine interest in applying AI within a business or leadership context.
Target Audience
- Senior management and heads of departments.
- General managers and executive leadership.
- Leaders overseeing digitalization and transformation programs.
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.
