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Course Outline
AI Fundamentals: Core Concepts, Varieties and Common Myths
- Defining the scope and boundaries of artificial intelligence
- Distinguishing between narrow AI and general AI
- Understanding machine learning, deep learning, and data science
- Explaining machine learning mechanics without technical jargon
Generative AI and AI Agents in a Business Context
- The capabilities and constraints of generative AI
- The mechanics of AI agents and their functionality
- Typical business applications of generative AI
- Understanding hallucinations and the current limitations of AI tools
Data Readiness: The Bedrock of AI Success
- Understanding structured and unstructured data types
- Key dimensions of data quality
- Essential data governance principles for managers
- The rationale for prioritizing data readiness before AI implementation
Identifying Where AI Drives Business Value
- Using the AI opportunity matrix
- Conducting value chain analysis for potential AI use cases
- Mapping primary and supporting business activities
- Identifying processes that offer the highest value potential
AI Success Stories and Key Takeaways
- Real-world AI applications across various business functions
- Factors that contributed to successful AI implementations
- Common failure patterns and strategies to mitigate them
Workshop: Discovering AI Opportunities by Department
- Mapping departmental processes and identifying pain points
- Brainstorming AI use case ideas for each business area
- Filling out an AI opportunity canvas
- Collaborating across departments to share and discuss insights
Prioritizing AI Use Cases for Optimal Value
- Scoring use cases based on value versus feasibility
- Balancing quick wins with strategic long-term investments
- Utilizing the AI project funnel approach
- Selecting the initial use cases to execute
AI Governance: Structure, Committees and Accountability
- Determining the appropriate leadership for AI initiatives
- Defining governance roles, committees, and duties
- Choosing between a Center of Excellence and distributed ownership models
- Adopting best practices for effective AI governance
Security, Risk Management and Responsible AI
- Navigating information security and data protection requirements
- Conducting risk assessments for AI projects
- Applying ethical guidelines for responsible AI usage
- Building trust in AI systems
Cultivating an AI-Ready Organization
- Evaluating the organization’s AI maturity level
- Developing the skills and competencies needed for the AI journey
- Managing change and preparing the culture for AI adoption
- Implementing the AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Synthesizing the identified opportunity map
- Establishing phases, quick wins, and key milestones
- Assigning ownership, defining metrics, and setting governance checkpoints
- Finalizing the initial roadmap and outlining next steps
Requirements
- No previous technical or programming experience is necessary.
- A genuine interest in applying AI within business and management settings.
Intended Audience
- Senior managers and heads of departments.
- General managers and executive-level leaders.
- Professionals overseeing digitalization and transformation projects.
16 Hours
Testimonials (1)
The trainer is patient and very helpful. He knows the topic well.
