Course Methodology

Participants will engage in a dynamic learning environment, characterized by collaborative real world case studies, hands on exercises, and strategic discussions. 

Course Objectives

By the end of the course, participants will be able to:

• Grasp the Fundamentals of AI: Understand AI's comprehensive landscape, including generative AI and large language models, and its significance in modern business
• Leverage AI Across the Business Value Chain: Identify and apply AI-driven strategies to enhance operational efficiency and innovation
• Unravel AI Technologies and Algorithms: Gain insights into the mechanisms driving AI solutions, tailored for managerial understanding rather than technical expertise
• Implement AI Best Practices: Learn the critical steps and methodologies for successful AI project management, including AI governance and MLOps frameworks
• Build AI Competence: Assess and develop the essential skills and competencies needed to lead AI initiatives within your organization
• Facilitate AI-centric Discussions: Engage effectively with both business and technical teams on AI-related endeavors
• Craft and Execute an AI Strategy: Develop a comprehensive strategy to transform your organization into an AI-driven enterprise

Target Audience

This course is designed for senior, middle and high potential management who recognize that digital transformation and AI is unavoidable; and for those who understand that continuous improvement, innovation and disruption is part of doing business and want to be prepared and reap the benefits of Artificial Intelligence.

In short, this course is for managers wanting to identify what AI can do for them and to drive Digital Transformation, rather than understand the technical methodologies of what happens underneath its hood.

Understanding of basic technology concepts such as data and cloud is helpful but not required.

Target Competencies

• AI Best Practice Application
• AI Change Management
• AI Business Translator
• AI Project Management

Introduction to Artificial Intelligence (AI), Machine Learning (ML) and Data Science

• AI in a historical setting and combinatorial technologies
• Human and artificial intelligence
• Introduction to AI, concepts, narrow and general AI
• Different types of AI, including generative AI
• The thinking in AI: Machine learning

Advanced Analytics vs Artificial Intelligence

• Gartner’s  ascendancy model
• 4 types of data analytics
• Analytics value chain

Algorithms without technical jargon

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Transformer and large language models

Data as fuel for AI

• Structured and unstructured data
• The 5 V’s of data
• Importance of quality data
• Data management and governance

AI and robotics

• 4 rational agents
• Intelligent agents
• Robotic paradigms
• Agents, robotics and reinforcement learning

AI opportunities

• Successful use cases by Porter’s value chain
• Successful use cases by technology
• Natural language processing
• Image recognition

Ideation of AI projects

• AI funnel process
• Several idea generation approaches
• Prioritizing projects
• AI project canvas

Running  AI projects

• Machine learning life cycle
• AI machine learning canvas
• Build or buy decisions

How to transform to an AI ready organization

• AI strategy and framework
• Dimensions of the AI framework
• Practical approach to assess AI maturity
• Best organizational structures
• Benefits of an AI Center of Excellence
• Skills and competencies

AI, risks, opportunities, ethics and sustainability

• Universal design
• Challenges and risks, technology readiness levels
• Ethical and trustworthy AI
• 3 areas of sustainability and 17 UN goals