Course Outline
Core AI Concepts: Definitions, Categories and Myths
- Distinguishing the reality of artificial intelligence from common misconceptions
- Comparing narrow AI with general AI
- Overview of machine learning, deep learning, and data science
- Explaining machine learning mechanisms without technical terminology
Business Applications of Generative AI and AI Agents
- Understanding the strengths and boundaries of generative AI
- The mechanics of AI agents
- Typical business uses of generative AI
- Addressing hallucinations and current technological limitations
Data Preparedness: The Basis for AI
- Differentiating between structured and unstructured data
- Key aspects of data quality
- Essential data governance principles for managers
- The critical importance of data readiness before AI deployment
Driving Business Value with AI
- Utilizing the AI opportunity matrix
- Value chain analysis tailored for AI use cases
- Focusing on primary and supporting business activities
- Identifying processes that yield the highest value
AI Success Stories and Key Takeaways
- Examining real-world AI implementations across various functions
- Factors contributing to successful AI adoptions
- Recognizing common pitfalls and strategies to prevent them
Practical Session: Spotting AI Opportunities by Department
- Mapping departmental workflows and identifying bottlenecks
- Brainstorming AI use case concepts for each business area
- Completing an AI opportunity canvas
- Collaborating and exchanging insights across departments
Ranking AI Use Cases for Optimal Impact
- Scoring based on value versus feasibility
- Balancing quick wins with long-term strategic investments
- Utilizing the AI project funnel
- Choosing the initial use cases to implement
AI Governance: Structures, Committees and Ownership
- Determining leadership structures for AI within the organization
- Defining governance roles, committees, and duties
- Centralized Centers of Excellence versus distributed accountability
- Standard practices for effective AI governance
Security, Risk Management and Responsible AI
- Compliance with information security and data protection regulations
- Conducting risk assessments for AI projects
- Adhering to ethical standards and responsible AI practices
- Developing trustworthy AI systems
Cultivating an AI-Ready Workforce
- Evaluating current AI maturity levels
- Developing necessary skills and competencies for the AI journey
- Managing change and fostering cultural adaptation
- Implementing the AI strategy cycle
Practical Session: Formulating the AI Implementation Roadmap and Action Plan
- Synthesizing the opportunity map
- Setting out phases, immediate wins, and key milestones
- Designating owners, KPIs, and governance review points
- Drafting the initial roadmap and defining subsequent actions
Requirements
- Background in technical skills or programming is not necessary.
- A desire to leverage AI within business or management settings.
Target Audience
- Senior management and department leaders.
- General managers and C-suite executives.
- Professionals overseeing digital transformation and modernization projects.
Testimonials (2)
correct way of prompting and including guardrails in instructions.
YEO SHI MIN - ST Engineering Aerospace Ltd
Course - ChatGPT and Microsoft 365 Copilot for Advanced Productivity
Understand AI function n tools to make our job easier. Need to improved AI Chubb such as make analysis n creating presentation
