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Course Outline
AI Fundamentals: Key Concepts, Varieties and Common Myths
- Defining what artificial intelligence is, and what it is not
- Distinguishing between narrow AI and general AI
- Overview of machine learning, deep learning, and data science
- Understanding machine learning mechanisms without technical jargon
Generative AI and AI Agents in Business Contexts
- Capabilities and inherent limitations of generative AI
- How AI agents function and operate
- Typical business applications for generative AI tools
- Addressing hallucinations and the current boundaries of AI technology
Data Readiness: The Bedrock of AI Success
- Differentiating between structured and unstructured data
- Data quality standards and their critical dimensions
- Essentials of data governance for management teams
- The importance of establishing data readiness prior to AI deployment
Identifying Where AI Drives Business Value
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for potential AI use cases
- Focus on primary and supporting business activities
- Identifying processes that offer the highest value potential
AI Success Stories and Key Takeaways
- Real-world examples of AI applications across various business functions
- Factors that contributed to successful AI implementations
- Identifying common failure patterns and strategies to prevent them
Workshop: Spotting AI Opportunities by Department
- Mapping departmental processes and identifying pain points
- Brainstorming AI use case ideas for each business area
- Completing an AI opportunity canvas
- Cross-departmental sharing and discussion of findings
Prioritizing AI Use Cases for Maximum Impact
- Scoring projects based on value versus feasibility
- Balancing quick wins with long-term strategic investments
- Navigating the AI project selection funnel
- Choosing the initial set of use cases to pursue
AI Governance: Roles, Committees and Accountability
- Determining who should lead AI initiatives within the organization
- Defining governance roles, committee structures, and responsibilities
- Comparing Centers of Excellence models with distributed ownership approaches
- Best practices for establishing effective AI governance
Security, Risk Management and Responsible AI
- Information security protocols and data protection constraints
- Conducting risk assessments for AI projects
- Ethical guidelines and the principles of responsible AI usage
- Building trust and reliability in 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
- Understanding the continuous AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating findings from the opportunity map
- Defining implementation phases, quick wins, and key milestones
- Assigning ownership, defining metrics, and setting governance checkpoints
- Finalizing the initial roadmap and outlining next steps
Requirements
- No existing technical background or programming skills are necessary.
- A genuine interest in applying AI principles within a business or managerial setting.
Target Audience
- Senior management and departmental leaders.
- General managers and C-suite executives.
- Leaders overseeing digital transformation and modernization initiatives.
16 Hours
Testimonials (1)
The trainer is patient and very helpful. He knows the topic well.
