Course Outline
AI Fundamentals: Key Concepts, Categories, and Common Misconceptions
- Defining the scope of artificial intelligence and its boundaries
- Distinguishing between Narrow AI and General AI
- Overview of machine learning, deep learning, and data science
- Explaining machine learning mechanics in plain language
Generative AI and AI Agents in a Business Context
- Understanding the strengths and limitations of generative AI
- The mechanics and functionality of AI agents
- Typical business applications for generative AI
- Addressing hallucinations and the current constraints of AI tools
Data Readiness: The Essential Foundation for AI
- The distinction between structured and unstructured data
- Data quality metrics and their critical dimensions
- Key data governance principles for management
- The importance of establishing data readiness prior to AI adoption
Identifying Business Value Opportunities with AI
- Utilizing the AI opportunity matrix
- Conducting value chain analysis for AI applications
- Examining primary and supporting business activities
- Focusing on processes that yield the highest value
AI Success Stories and Key Takeaways
- Examining real-world AI applications across various departments
- Analyzing the factors behind successful AI implementations
- Identifying common failure patterns and strategies to mitigate them
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
- Collaboratively discussing findings across departments
Prioritizing AI Use Cases for Optimal Value
- Scoring initiatives based on value versus feasibility
- Balancing quick wins against strategic long-term investments
- Implementing the AI project funnel approach
- Selecting initial use cases for immediate pursuit
AI Governance: Roles, Committees, and Accountability
- Identifying the appropriate leadership for AI in the organization
- Defining governance roles, committees, and accountability structures
- Comparing a Center of Excellence model with distributed ownership
- Adopting best practices for effective AI governance
Security, Risk Management, and Responsible AI
- Complying with information security and data protection requirements
- Conducting risk assessments for AI initiatives
- Adhering to ethical guidelines and responsible AI usage
- Building frameworks for trustworthy AI
Establishing an AI-Ready Organization
- Evaluating the current AI maturity level
- Developing necessary skills and competencies for the AI journey
- Managing change and assessing cultural readiness
- Implementing the AI strategy cycle
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Synthesizing the data from the opportunity map
- Structuring phases, quick wins, and key milestones
- Assigning ownership, defining metrics, and setting governance checkpoints
- Finalizing the initial roadmap and defining next steps
Requirements
- No background in technical skills or programming is required.
- A genuine interest in leveraging AI within a business or managerial context.
Target Audience
- Senior managers and department heads.
- General managers and C-suite executives.
- Leaders overseeing digitalization and transformation projects.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
Contact us for an exact quote and to hear our latest promotions
Testimonials (2)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
