Course Outline
AI Fundamentals: Core Concepts, Types, and Common Misconceptions
- Understanding what AI is and what it is not
- Distinguishing between Narrow AI and General AI
- Exploring machine learning, deep learning, and data science
- Demystifying machine learning processes without technical jargon
Generative AI and AI Agents in a Business Context
- Assessing the capabilities and constraints of generative AI
- Understanding the mechanics of AI agents
- Reviewing common business applications of generative AI
- Addressing hallucinations and the current boundaries of AI tools
Data Readiness: The Essential Foundation for AI
- Differentiating between structured and unstructured data
- Evaluating data quality and its critical dimensions
- Key data governance principles for managers
- The importance of establishing data readiness before AI deployment
Unlocking Business Value with AI
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for AI use cases
- Focusing on primary and supporting business activities
- Identifying processes that offer the highest value potential
AI Success Stories and Key Lessons
- Examining real-world AI applications across various business functions
- Analyzing the factors behind successful implementations
- Recognizing common failure patterns and strategies to mitigate them
Workshop: Identifying AI Opportunities by Department
- Mapping departmental processes and identifying pain points
- Brainstorming AI use case ideas for specific business areas
- Completing an AI opportunity canvas
- Sharing and discussing insights across departments
Prioritizing AI Use Cases for Maximum Impact
- Scoring based on value versus feasibility
- Balancing quick wins with long-term strategic investments
- Applying the AI project funnel model
- Selecting the initial use cases to pursue
AI Governance: Roles, Committees, and Accountability
- Determining who should lead AI initiatives in the organization
- Defining governance roles, committees, and responsibilities
- Comparing a Center of Excellence model with distributed ownership
- Reviewing 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
- Implementing ethical guidelines and responsible AI usage
- Building trust through reliable AI systems
Building an AI-Ready Organization
- Evaluating the organization's AI maturity level
- Developing the necessary skills and competencies for the AI journey
- Managing change and ensuring cultural readiness
- Implementing the AI strategy cycle
Workshop: Creating the AI Implementation Roadmap and Action Plan
- Consolidating the identified opportunity map
- Defining implementation phases, quick wins, and key milestones
- Assigning owners, setting metrics, and establishing governance checkpoints
- Finalizing the initial roadmap and outlining next steps
Requirements
- No prior technical background or programming experience is necessary.
- A genuine interest in applying AI within a business or management setting.
Target Audience
- Senior managers and department heads.
- General managers and executive leaders.
- Professionals leading 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.
