Generative AI for Business: From Awareness to Adoption

This training provides a practical introduction to artificial intelligence and machine learning, moving from foundational concepts to real business applications. Participants will explore how AI systems learn from data, how machine learning models are developed, and how generative AI differs from traditional analytical models. The course uses practical business examples to demonstrate how AI can support forecasting, classification, segmentation, anomaly detection, automation, content generation, and decision support across different functions.

Why Should You Attend?

Organizations are increasingly adopting AI, but many professionals still need a clear understanding of how the technology works and where it can create business value. This course helps participants distinguish between different forms of AI, understand common machine learning techniques, evaluate practical use cases, and contribute more effectively to AI-related discussions and initiatives within their organizations.

Educational Approach

● Plain-language explanations of AI, machine learning, and generative AI concepts.
● Practical examples of supervised and unsupervised learning.
● Business cases covering classification, forecasting, clustering, and anomaly detection.
● Live demonstrations of AI applications across business functions, and interactive discussions on identifying and prioritizing suitable AI use cases.

Prerequisites

No prior technical or programming experience is required. A general understanding of business processes and data will be helpful.

Course Agenda

● Session 1: Introduction to artificial intelligence, machine learning, deep learning, and generative AI.
● Session 2: Understanding data, features, models, training, testing, and model outputs.
● Session 3: Supervised learning, including classification and regression, with practical business examples.
● Session 4: Unsupervised learning, including clustering, segmentation, and pattern identification.
● Session 5: Generative AI, Large Language Models, and common business applications.