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Course Outline
AI Fundamentals: Key Concepts, Categories, and Common Misconceptions
- Defining what artificial intelligence is—and is not
- Distinguishing between Narrow AI and General AI
- Understanding Machine Learning, Deep Learning, and Data Science
- Explaining how Machine Learning operates without technical jargon
Generative AI and AI Agents in a Business Context
- The capabilities and inherent limitations of Generative AI
- How AI agents function
- Typical business applications for Generative AI
- Understanding hallucinations and the boundaries of current tools
Data Readiness: The Cornerstone of AI Success
- Differentiating between structured and unstructured data
- Key dimensions of data quality
- Essential Data Governance principles for managers
- The importance of data readiness prior to AI deployment
Identifying Where AI Delivers Business Value
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for AI use cases
- Primary and supporting business activities
- Processes that yield the highest value
AI Success Stories and Key Lessons
- Real-world AI applications across various business functions
- Factors that contribute to successful implementations
- Common failure patterns and strategies to prevent them
Workshop: Discovering 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
- Collaboratively reviewing and discussing findings across departments
Prioritizing AI Use Cases for Optimal Value
- Scoring based on value versus feasibility
- Distinguishing between quick wins and strategic long-term bets
- Applying the AI project funnel
- Selecting the initial use cases to prioritize
AI Governance: Roles, Committees, and Accountability
- Determining who should lead AI initiatives within the organization
- Defining governance roles, committees, and responsibilities
- Center of Excellence models versus distributed ownership
- Best practices for establishing AI governance
Security, Risk, and Responsible AI
- Navigating information security and data protection constraints
- Conducting risk assessments for AI initiatives
- Ethical guidelines and the responsible use of AI
- Building trustworthy AI systems
Building an AI-Ready Organization
- Assessing current AI maturity levels
- Skills and competencies required for the AI journey
- Change management and ensuring cultural readiness
- The continuous AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating the identified opportunity map
- Defining implementation phases, quick wins, and milestones
- Assigning owners, setting metrics, and establishing governance checkpoints
- Finalizing the initial roadmap and defining next steps
Requirements
- No prior background in technology or programming is necessary.
- A keen interest in leveraging AI within a business or management setting.
Target Audience
- Senior managers and department heads.
- General managers and C-suite executives.
- Leaders overseeing digitalization and transformation initiatives.
16 Hours
Testimonials (1)
The trainer is patient and very helpful. He knows the topic well.
