Artificial Intelligence (AI) for Business Professionals

Overview:

Introduction

Petabytes of data are being generated by society and businesses; thanks to artificial intelligence (AI), we can use this data to enhance wellbeing, boost revenue, and cut expenses. With the help of contemporary technology, we may use both internal and external, organized and unstructured data, and apply Artificial Intelligence to open up new possibilities for making predictions, improving decision-making, enhancing business performance, and enhancing human capabilities.

This course equips participants with the AI literacy they need to be the business AI leaders in their organizations. Participants will gain an understanding of AI concepts and use cases, be able to communicate intelligently with data specialists, develop an AI strategy, build an organization that is AI ready, set up and manage AI projects, and evaluate whether to make or buy tooling.

Course Objectives

At the end of this course, participants will be able to:

• Describe the idea of AI and all of its uses.
• Utilize various AI applications throughout the corporate value chain.
• Showcase the AI-related technologies and algorithms.
• Using an AI project's efforts, implement best practices
• Examine the skills and abilities that are both available and required.
• Discuss pertinent issues in length with business and data experts.
• Create and implement an AI strategy, and build an organization that is AI ready.

Targeted Audience

This course is intended for senior, medium, and high potential management who realize that disruption, innovation, and continuous improvement are all essential components of doing business and who want to prepare for and benefit from Artificial Intelligence.

In other words, rather than understanding the technical approaches of what occurs inside its body, this course is for managers who want to recognize what AI can accomplish for them and to drive Digital Transformation.

Course Outline:

Unit 1:Introduction to Artificial Intelligence (AI), Machine Learning (ML) and Data Science

• AI in historical setting and combinatorial technologies
• Introduction to AI, concepts, narrow and general AI
• Different types of AI
• AI - sense, reason, act
• The thinking in AI: Machine learning

Unit 2: Advanced Analytics vs Artificial Intelligence

• Looking back, now, forward
• 4 types of data analytics
• Analytics value chain

Unit 3: Data as fuel for AI

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

Unit 4: Algorithms but without technical jargon

• Supervised learning
• Unsupervised learning
• Reinforcement learning

Unit 5: The data engineering platform

• Just enough to understand the data architecture
• Big data reference architecture
• 3 categories of data usage

Unit 6: AI opportunity matrix

• Successful use cases by Porter’s value chain
  • Primary activities
  • Supporting activities
• Successful use cases by technology
  • NLP
  • Image recognition
  • Machine learning

Unit 7: Ideation of AI projects

• AI Funnel process
• Several idea generation approaches
• Prioritize projects
• AI project canvas

Unit 8: How to transform to an AI ready organization

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

Unit 9: Running of AI projects

• Machine learning life cycle
• AI machine learning canvas
• When to make and when to buy AI solutions

Unit 10: AI and ethics

• Risks of AI
• Ethical guidelines
• Realizing trustworthy AI