Course Outline
AI Fundamentals: Core Concepts, Types, and Common Misconceptions
- Distinguishing what artificial intelligence is and is not.
- Comparing Narrow AI with General AI.
- Understanding machine learning, deep learning, and data science.
- Explaining machine learning principles without technical jargon.
Generative AI and AI Agents in a Business Context
- Assessing the capabilities and inherent limitations of generative AI.
- Understanding the mechanics and function of AI agents.
- Reviewing common business applications of generative AI.
- Addressing hallucinations and the boundaries of current tools.
Data Readiness: The Foundation for AI
- Differentiating between structured and unstructured data.
- Evaluating data quality and its critical dimensions.
- Essentials of data governance for management teams.
- Understanding why data readiness must precede AI deployment.
Where AI Drives Business Value
- Utilizing the AI opportunity matrix.
- Applying value chain analysis to identify AI use cases.
- Analyzing primary and supporting business activities.
- Identifying processes that yield the highest value.
AI Success Stories and Key Learnings
- Reviewing real-world AI applications across different business functions.
- Analyzing factors behind successful implementations.
- Recognizing common failure patterns and strategies to avoid them.
Workshop: Identifying AI Opportunities by Department
- Mapping departmental processes and identifying pain points.
- Generating 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 against strategic long-term bets.
- Utilizing the AI project funnel.
- Selecting the initial use cases to pursue.
AI Governance: Roles, Committees, and Accountability
- Determining who should lead AI initiatives within the organization.
- Defining governance roles, committees, and responsibilities.
- Comparing Center of Excellence models with distributed ownership.
- Adopting best practices for 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 usage.
- Building trustworthy AI systems.
Building an AI-Ready Organization
- Assessing current AI maturity levels.
- Identifying required skills and competencies for the AI journey.
- Managing change and assessing cultural readiness.
- Implementing the AI strategy cycle.
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating the AI opportunity map.
- Defining implementation phases, quick wins, and key milestones.
- Assigning owners, metrics, and governance checkpoints.
- Finalizing the initial roadmap and defining next steps.
Requirements
- No prior technical or programming experience is necessary.
- A genuine interest in applying AI within a business or management setting is beneficial.
Target Audience
- Senior managers and department heads.
- General managers and executive leadership.
- 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.
