Course Outline
AI Foundations: Key Concepts, Categories and Common Misunderstandings
- Distinguishing what artificial intelligence truly is and what it is not.
- Comparing narrow AI with general AI.
- Exploring machine learning, deep learning, and data science.
- Understanding how machine learning functions without relying on technical jargon.
Generative AI and AI Agents in the Business Landscape
- Evaluating the capabilities and constraints of generative AI.
- Understanding the mechanics and operation of AI agents.
- Reviewing prevalent business applications of generative AI.
- Addressing hallucinations and the boundaries of current tooling.
Data Readiness: The Cornerstone of AI
- Differentiating between structured and unstructured data.
- Assessing data quality and its essential dimensions.
- Key data governance principles for managers.
- Why data readiness must precede AI adoption.
Identifying Where AI Drives Business Value
- Utilising the AI opportunity matrix.
- Applying value chain analysis to AI use cases.
- Examining primary and supporting activities.
- Identifying processes that yield the highest value.
AI Success Stories and Key Takeaways
- Real-world AI applications across diverse business functions.
- Factors that contributed to the success of key implementations.
- Common failure patterns and strategies to avoid them.
Workshop: Discovering AI Opportunities by Department
- Mapping departmental processes and identifying pain points.
- Generating AI use case ideas for each business area.
- Completing an AI opportunity canvas.
- Sharing and debating findings across different departments.
Prioritising AI Use Cases for Optimal Value
- Scoring based on value versus feasibility.
- Balancing quick wins against strategic investments.
- Navigating 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 organisation.
- Defining governance roles, committees, and responsibilities.
- Weighing a Center of Excellence against distributed ownership models.
- Best practices for effective AI governance.
Security, Risk Management and Responsible AI
- Navigating information security and data protection constraints.
- Conducting risk assessments for AI projects.
- Adhering to ethical guidelines and responsible AI usage.
- Cultivating trustworthy AI systems.
Cultivating an AI-Ready Organisation
- Evaluating current AI maturity levels.
- Developing necessary skills and competencies for the AI journey.
- Managing change and ensuring cultural readiness.
- Implementing the AI strategy cycle.
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating the opportunity map.
- Defining phases, quick wins, and key milestones.
- Assigning ownership, metrics, and governance checkpoints.
- Producing the initial roadmap and outlining next steps.
Requirements
- No prior technical expertise or programming background is necessary.
- A strong interest in leveraging AI within business and management contexts.
Target Audience
- Senior managers and department heads.
- General managers and executive leadership.
- Leaders overseeing digitalisation and transformation projects.
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.
