Course Outline
AI Fundamentals: Concepts, Categories and Common Misconceptions
- Distinguishing what artificial intelligence is, and what it is not
- Narrow AI contrasted with general AI
- Overview of machine learning, deep learning and data science
- Understanding how machine learning works without technical jargon
Generative AI and AI Agents in Business Contexts
- Capabilities and inherent limitations of generative AI
- How AI agents function and interact
- Typical business applications of generative AI
- Understanding 'hallucinations' and the current boundaries of AI tools
Data Readiness: The Bedrock of AI Success
- Differences between structured and unstructured data
- Data quality standards and its critical dimensions
- Essential data governance principles for managers
- The importance of data readiness before deploying AI
Generating Business Value with AI
- Utilising the AI opportunity matrix
- Value chain analysis to identify AI use cases
- Focusing on primary and supporting business activities
- Identifying processes that yield the highest value
AI Success Stories and Key Lessons Learned
- Real-world AI applications across various business functions
- Key factors behind successful AI implementations
- Common failure patterns and strategies to prevent 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
- Collaborative sharing and discussion of findings across departments
Prioritising AI Use Cases for Maximum Impact
- Scoring based on value versus feasibility
- Balancing quick wins with strategic long-term bets
- Applying the AI project funnel methodology
- Selecting the initial use cases for implementation
AI Governance: Roles, Committees and Accountability
- Determining who should lead AI initiatives within the organization
- Defining governance roles, committees and specific responsibilities
- Comparing a Centre of Excellence approach versus distributed ownership
- Best practices for effective AI governance
Security, Risk and Responsible AI Practices
- Navigating information security and data protection constraints
- Conducting risk assessments for AI projects
- Adhering to ethical guidelines and responsible AI usage
- Building trust in AI systems
Creating an AI-Ready Organization
- Assessing current AI maturity levels
- Developing necessary skills and competencies for the AI journey
- Managing change and preparing for cultural readiness
- Implementing the AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating the identified opportunity map
- Defining implementation phases, quick wins and key milestones
- Assigning ownership, success metrics and governance checkpoints
- Finalising the initial roadmap and determining next steps
Requirements
- No prior technical or programming experience is necessary.
- A genuine interest in applying AI within a business or management environment.
Target Audience
- Senior managers and department heads.
- General managers and executive-level leaders.
- Professionals driving digitalisation and business 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.
