AI for Executives: AI for Business Decision Making

Overview

AI for Business Decision Making equips managers and executives with practical, non-technical skills to lead AI-driven decisions across the enterprise. You’ll learn how supervised, unsupervised, and reinforcement learning improve decision accuracy; how to prepare data and engineer features; and how to train, validate, and integrate models into existing workflows and systems via APIs and automation. Through guided, hands-on notebooks, you’ll experiment with predictive analytics, Natural Language Processing (including sentiment analysis), and Large Language Models (LLMs) for real executive use cases—from customer feedback to process optimization and risk prediction. The course also covers governance and ethics (privacy, security, bias, explainability), human-in-the-loop design, and measuring business impact. Advanced topics introduce genetic/evolutionary methods and long-term reward optimization with reinforcement learning to inform strategy and planning. By the end, you’ll be able to select the right technique for a decision problem, evaluate model reliability, integrate AI into business processes, and build an actionable roadmap for sustainable, responsible AI adoption.

Syllabus

The Fundamental of AI and Decision Making

This module introduces participants to the essential concepts of Artificial Intelligence (AI) and its role in shaping business decision-making. Covering AI types, historical development, and ethical considerations, participants gain foundational knowledge crucial for understanding AI's impact on decision accuracy. This module sets the stage for practical applications and advanced techniques in subsequent sessions, providing a solid foundation for navigating the intersection of AI and effective decision-making in business.

Implementing AI Decision Models in Business

This module is dedicated to the practical aspects of implementing AI decision models within a business framework. Participants will be guided through the process of building and training different types of AI models, encompassing model identification, data preparation, feature engineering, and the application of model training and validation techniques. The module further explores the seamless integration of AI into existing business systems, focusing on workflows, API integration, and efficient data flow management.

AI Technologies for Decision Support

This module moves from model‑building mechanics to strategic, data‑driven decision support. You will compare supervised, unsupervised, and reinforcement techniques, apply natural‑language and predictive analytics—including large language models—to real business questions, and weigh machine‑learning recommendations against human cognitive factors to select the most effective approach for each decision context.