AI for Business Professionals: Leveraging Artificial Intelligence for Success

Introduction:

In the contemporary era, data generation by societies and organizations has reached unprecedented levels. Artificial Intelligence (AI) leverages this data to enhance well-being, boost revenues, and reduce costs. By structuring and analyzing both structured and unstructured data, AI unlocks new capabilities, improves forecasts, and streamlines decision-making processes, thereby amplifying human skills and optimizing organizational performance.

This course, "AI for Business Professionals: Leveraging Artificial Intelligence for Success," focuses on mastering AI for practical business applications. It emphasizes moving beyond isolated proofs of concept to develop a comprehensive, organization-wide AI strategy. Participants will learn to identify optimal AI opportunities, prepare their organizations for AI integration, and implement actionable plans for successful AI adoption.

By the end of this course, participants will gain essential AI literacy to lead AI initiatives, understand core AI concepts, and devise strategies to harness AI for organizational growth.

Objectives:

At the end of this Artificial Intelligence (AI) for Business Professionals course, participants will be able to:

• Define AI and its impact and applications.
• Apply various AI approaches throughout the business value chain.
• Demonstrate knowledge of key AI technologies and algorithms.
• Implement best practices for managing AI projects.
• Identify skills and competencies necessary for AI integration.
• Engage effectively with business and data specialists on AI topics.
• Develop and execute an AI strategy to foster organizational readiness.

Training Methodology:

• Lectures
• Case Studies
• Hands-on Activities
• Group Discussions
• Demonstration Sessions
• Situational Exercises
• Panels of Specialists

Course Outline:

Unit 1: Overview of Artificial Intelligence (AI), Machine Learning (ML), and Data Science

• Historical development of AI and combinatorial technologies
• Types and levels of AI: an overview
• Evolution of AI development stages
• Components of AI
• Machine Learning as a subset of AI

Unit 2: Advanced Analytics vs. Artificial Intelligence

• Technological roots of analytics
• Types of data analysis
• Analytics as a process

Unit 3: Understanding Algorithms Without Technical Jargon

• Supervised, unsupervised, and reinforcement learning explained simply

Unit 4: Data as Fuel for AI

• Structured vs. unstructured data
• The five Vs of data
• Importance of data governance

Unit 5: The Data Engineering Platform

• Overview of data architecture
• Structure of large-scale data systems
• Three spheres of data use

Unit 6: AI Opportunity Matrix

• Mapping opportunities within Porter’s Value Chain
• Core and complementary activities

Unit 7: Successful Use Cases by Technology

• Natural Language Processing (NLP)
• Image classification technology
• Machine learning applications

Unit 8: Ideation of AI Projects

• AI funnel process for idea generation
• Methods for generating and prioritizing AI project ideas
• AI project canvas

Unit 9: Transitioning to an AI-Ready Organization

• AI strategy cycle
• AI framework structure
• Measuring organizational readiness for AI
• Structuring the organization for AI adoption
• Establishing an AI Center of Excellence
• Essential competencies for AI readiness

Unit 10: AI and Ethics

• Understanding AI risks
• Acceptable Use Policies for AI
• AI Governance
• Managing AI-related risks
• Developing safe AI systems