AI in Business

As Artificial Intelligence becomes central to gaining a competitive advantage, business leaders must navigate a complex landscape of moral, legal, and reputational risks. This course explores the strategic intersection of AI, ethics, and corporate governance. Students will examine the socio-economic implications of algorithmic decision-making, including bias, privacy, surveillance, and the future of work.

Through the lens of business strategy and moral philosophy, this course prepares future leaders to operationalize responsible AI. It will analyze real-world case studies to understand how ethical failures can destroy brand value. A component of this graduate course is a research paper on a topic covered in class, which will allow students to gain further insights into the ethical aspects of AI and its practical applications in the business world. Students will work on a research paper in this class.

This course equips students with skills to turn data into managerial insight and impact. Emphasizing the full analytics life-cycle, from problem framing, data acquisition, exploratory analysis, modeling, and executive storytelling, students will learn to select appropriate analytical methods (descriptive, predictive, prescriptive), interpret results, and translate them into actionable recommendations. It uses standard software to provide applications in marketing, operations, finance, and other areas in different industries. The focus is on practical business applications of data analytics. Students will work on a research paper in this class.

This course provides a framework for leveraging Artificial Intelligence as a core driver of business strategy and operational excellence. It provides hands-on exposure to major AI and generative AI technologies and their application in business. The course covers topics such as deep learning, large language models, prompt design, agentic AI, and the business aspects of leveraging and deploying AI.

Through case studies and hands-on projects, the course examines the transformative role of AI in marketing, finance, operations, supply chain, and human resources. By the end of the course, students will be equipped to contribute to AI-driven digital transformation and to present use cases for AI adoption that align with organizational goals, competitive positioning, and sustainable growth. Graduate students will have an additional research/project component in the class and conduct more advanced work.

This course gives graduate students practical programming skills to design, prototype, and evaluate AI-enabled business solutions. Students progress from core scripting to understanding how predictive models are built and integrated into Large Language Models (LLMs) and other AI services via APIs (e.g., OpenAI, Anthropic, vector databases). Emphasis is placed on the end-to-end product lifecycle: problem framing, technical feasibility, data governance, rapid prototyping, evaluation, cost/performance trade‑offs, and deployment.

This course is part of the master’s program in AI for Business that JSOB is proposing. This course bridges the gap between technical Machine Learning (ML) algorithms and strategic business applications. The curriculum focuses on leveraging supervised, unsupervised, and reinforcement learning to drive competitive advantage. Students will explore the end-to-end ML pipeline, from data preparation and feature engineering to model selection, evaluation, and deployment.

The course emphasizes the “managerial interpretation” of ML outputs. Students will apply ML techniques to real-world business challenges, including customer churn prediction, credit scoring, demand forecasting, and market segmentation.

This course explores the role of Artificial Intelligence as a primary driver of corporate strategy and a catalyst for high-stakes managerial decision-making. While other courses in the program focus on the “how” of building and managing AI, this course focuses on the “why” and “where” of AI deployment to achieve sustainable competitive advantage.

Students will analyze how AI disrupts traditional industry structures, reshapes value chains, and enables new business models. The curriculum centers on AI-augmented decision-making, teaching future leaders how to integrate predictive and prescriptive AI outputs into strategic planning. Key topics include the economics of AI, the strategic implementation of Agentic AI (autonomous agents), and navigating “Human-AI” collaborative leadership.

This course is at the intersection of AI and finance. Students will learn to apply AI tools to investment research, portfolio construction, and risk management. They will also learn how to prepare and present complex outputs into transparent, decision-ready visuals. Emphasis is on building interactive dashboards, explaining model drivers, and communicating insights to investors and executive stakeholders. Students will use financial statements and financial markets data to produce persuasive, clear analyses suitable for decision-making activities in the short and long run in the finance world.

This course explores how Artificial Intelligence can, and has started to, transform healthcare delivery, operations, and strategy. Students will analyze clinical and administrative use cases, including clinical decision support, imaging, triage, population health, revenue cycle, scheduling, and supply chain, with a special focus on value capture, safety, and compliance. Regulatory oversight (HIPAA, FDA, others) is also considered. Students will learn how to use AI to identify opportunities for greater efficiency, productivity, clinical and financial ROI, and to design governance that reduces bias and improves safety.

This course bridges traditional accounting principles with today’s data-driven business environment. Students study the design, operation, and control of advanced Accounting Information Systems with a focus on leveraging data analytics to enhance financial reporting, auditing, and managerial decision-making. Moving beyond basic transaction processing, the course addresses CPA Evolution core competencies, including data management, automated internal controls, and audit analytics.

An advanced study of marketing theories concerning consumer motivations and behaviors in the marketplace. Emphasis is placed on consumer behavior and the major stimuli that influence it. Graduate students will work on a research project applying the knowledge gained in the course.