Course Outline
AI Fundamentals: Core Concepts, Variations, and Common Misconceptions
- Distinguishing between what artificial intelligence is and what it is not
- Narrow AI versus general AI
- The relationship between 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
- The mechanics and function of AI agents
- Typical business applications of generative AI
- Understanding hallucinations and the current boundaries of AI tools
Data Readiness: The Bedrock of AI Strategy
- Characterizing structured and unstructured data
- Key dimensions of data quality
- Essential data governance principles for managers
- The importance of establishing data readiness prior to AI adoption
Unlocking Business Value with AI
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for AI use cases
- Identifying primary and supporting business activities
- Recognizing processes that yield the highest value
AI Success Stories and Key Takeaways
- Real-world AI implementations across various business functions
- Factors that contribute to successful AI deployments
- Identifying common failure patterns and strategies to prevent them
Workshop: Mapping 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
- Collaborating to share and discuss findings across departments
Prioritizing AI Use Cases for Optimal Impact
- Scoring based on value versus feasibility
- Balancing quick wins against long-term strategic investments
- Utilizing the AI project funnel
- Selecting the initial use cases to pursue
AI Governance: Roles, Committees, and Accountability
- Determining the leadership structure for AI within the organization
- Defining governance roles, committees, and responsibilities
- Comparing Center of Excellence models versus distributed ownership
- Implementing best practices for AI governance
Security, Risk Management, and Responsible AI
- Navigating information security and data protection requirements
- Conducting risk assessments for AI initiatives
- Adhering to ethical guidelines and responsible AI usage
- Building trust in AI systems
Cultivating an AI-Ready Organization
- Evaluating the organization's AI maturity level
- Identifying 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
- Synthesizing the AI opportunity map
- Defining implementation phases, quick wins, and key milestones
- Assigning ownership, metrics, and governance checkpoints
- Creating the initial roadmap and outlining immediate next steps
Requirements
- No prior technical background or programming experience is necessary.
- A keen interest in applying AI within a business or management framework.
Target Audience
- Senior managers and department heads.
- General managers and C-suite executives.
- Leaders overseeing digitalization 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.
