Course Outline
AI Foundations: Core Concepts, Categories, and Common Misunderstandings
- Distinguishing what artificial intelligence can and cannot do.
- Comparing narrow AI with general AI.
- Understanding the relationship between machine learning, deep learning, and data science.
- Explaining how machine learning functions without relying on technical jargon.
Generative AI and AI Agents in the Enterprise
- Exploring the capabilities and inherent constraints of generative AI.
- Understanding AI agents and their operational mechanics.
- Reviewing standard business applications for generative AI.
- Navigating hallucinations and the current boundaries of AI tools.
Data Readiness: The Bedrock of AI
- Differentiating between structured and unstructured data.
- Evaluating data quality and its critical dimensions.
- Essential data governance principles for managers.
- The imperative of establishing data readiness prior to AI adoption.
Unlocking Business Value with AI
- Utilizing the AI opportunity matrix.
- Conducting value chain analysis for AI-driven use cases.
- Identifying primary and supporting activities.
- Recognizing the processes that yield the highest return on investment.
Case Studies: Success Stories and Key Lessons
- Examining real-world AI applications across various business functions.
- Analyzing the factors behind successful AI implementations.
- Identifying common failure patterns and strategies for prevention.
Workshop: Discovering 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 insights and fostering discussion across different departments.
Prioritizing AI Use Cases for Optimal Impact
- Scoring based on value versus feasibility.
- Balancing quick wins against long-term strategic bets.
- Applying the AI project funnel model.
- Selecting the initial use cases for execution.
AI Governance: Leadership, Committees, and Accountability
- Determining the appropriate leadership structure for AI.
- Defining governance roles, committee structures, and responsibilities.
- Choosing between a Center of Excellence and distributed ownership models.
- Implementing best practices for effective AI governance.
Security, Risk Management, and Responsible AI
- Navigating information security and data protection regulations.
- 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 organizational AI maturity.
- Developing the skills and competencies required for the AI journey.
- Managing change and assessing cultural readiness.
- Executing the AI strategy cycle.
Workshop: Formulating the AI Implementation Roadmap and Action Plan
- Synthesizing the consolidated opportunity map.
- Establishing implementation phases, quick wins, and key milestones.
- Assigning ownership, defining metrics, and setting governance checkpoints.
- Finalizing the initial roadmap and outlining immediate next steps.
Requirements
- No prior technical expertise or programming skills are necessary.
- A genuine interest in leveraging AI within a business or managerial setting.
Target Audience
- Senior management and department leaders.
- General managers and C-suite executives.
- Executives overseeing digital transformation and modernization efforts.
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.
