Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
AI Foundations: Key Concepts, Varieties, and Common Myths
- Distinguishing between what artificial intelligence is and is not
- Differences between narrow AI and general AI
- Overview of machine learning, deep learning, and data science
- Understanding machine learning mechanisms without complex technical jargon
Generative AI and AI Agents in a Business Context
- Examining the capabilities and inherent limitations of generative AI
- Understanding AI agents and their operational mechanics
- Typical business applications of generative AI tools
- Recognizing hallucinations and the current constraints of AI tools
Data Preparation: The Bedrock of AI Strategy
- Understanding structured versus unstructured data
- Assessing data quality and its critical dimensions
- Essential data governance principles for managers
- The critical importance of data readiness prior to AI adoption
Identifying Where AI Drives Business Value
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for AI applications
- Evaluating primary and support activities
- Identifying processes with the highest value generation potential
AI Success Stories and Key Takeaways
- Reviewing real-world AI applications across different business functions
- Analyzing the factors behind successful implementations
- Identifying common failure patterns and strategies to avoid them
Practical Workshop: Spotting 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
- Sharing and critiquing 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
- Navigating the AI project funnel
- Selecting the initial use cases to pursue
AI Governance: Roles, Oversight, and Accountability
- Determining who should lead AI initiatives in the organization
- Defining governance roles, committees, and specific responsibilities
- Choosing between a Center of Excellence and distributed ownership models
- Applying best practices in AI governance
Security, Risk Management, and Responsible AI
- Complying with information security and data protection regulations
- Conducting risk assessments for AI projects
- Adhering to ethical guidelines and responsible AI usage
- Developing trustworthy AI systems
Cultivating an AI-Ready Organization
- Evaluating current AI maturity levels
- Identifying required skills and competencies for the AI journey
- Managing change and ensuring cultural readiness
- Implementing the AI strategy cycle
Practical Workshop: Developing the AI Implementation Roadmap and Action Plan
- Synthesizing the opportunity map
- Defining phases, quick wins, and key milestones
- Assigning owners, success metrics, and governance checkpoints
- Finalizing the initial roadmap and outlining next steps
Requirements
- No previous technical background or programming skills are necessary.
- A general interest in leveraging AI within a business or management framework.
Target Audience
- Senior managers and department heads.
- General managers and executive-level leaders.
- Leadership responsible for digitalization and transformation projects.
16 Hours
Testimonials (1)
The trainer is patient and very helpful. He knows the topic well.
