Course Outline
AI Essentials: Key Concepts, Variations, and Common Myths
- Defining the scope and boundaries of artificial intelligence
- Distinguishing between narrow AI and general AI
- Overviews of machine learning, deep learning, and data science
- Understanding machine learning mechanics without technical jargon
Generative AI and AI Agents in the Business Context
- Exploring the capabilities and constraints of generative AI
- Understanding the functionality of AI agents
- Typical business applications of generative AI
- Navigating hallucinations and the boundaries of current tools
Data Preparation: The Cornerstone of AI
- Differences between structured and unstructured data
- Key dimensions of data quality
- Essential data governance concepts for managers
- The importance of establishing data readiness before AI adoption
Generating Business Value with AI
- The AI opportunity matrix
- Value chain analysis for identifying AI use cases
- Primary and auxiliary business activities
- Identifying processes with the highest potential value
AI Success Stories and Key Takeaways
- Real-world AI implementations across various functions
- Factors behind successful AI deployments
- Recognizing common failure patterns and mitigation strategies
Workshop: Discovering AI Opportunities by Department
- Mapping departmental processes and identifying pain points
- Brainstorming AI use cases for specific business areas
- Completing an AI opportunity canvas
- Collaborative review and discussion of findings
Prioritizing AI Use Cases for Optimal Impact
- Scoring based on value versus feasibility
- Choosing between quick wins and strategic long-term investments
- The AI project funnel approach
- Selecting the initial use cases to execute
AI Governance: Leadership, Committees, and Accountability
- Determining AI leadership within the organization
- Defining governance roles, committees, and duties
- Comparing Center of Excellence models with distributed ownership
- Best practices for establishing AI governance
Security, Risk Management, and Responsible AI
- Addressing information security and data protection requirements
- Conducting risk assessments for AI initiatives
- Ethical guidelines and responsible usage of AI
- Building trust in AI systems
Creating an AI-Ready Organization
- Evaluating organizational AI maturity
- Developing necessary skills and competencies
- Managing change and fostering cultural readiness
- The AI strategy lifecycle
Workshop: Developing the AI Execution Roadmap and Action Plan
- Aggregating the opportunity map
- Setting phases, quick wins, and key milestones
- Assigning owners, defining metrics, and establishing governance checkpoints
- Finalizing the initial roadmap and immediate next steps
Requirements
- No prior technical or programming background is necessary.
- A genuine interest in applying AI within business or management scenarios.
Target Audience
- Senior managers and department heads.
- General managers and C-level executives.
- Leaders overseeing digital transformation and modernization 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.
